{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Classification, Probabilities, ROC curves, and Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None):\n",
    "    if score_func:\n",
    "        gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)\n",
    "    else:\n",
    "        gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)\n",
    "    gs.fit(X, y)\n",
    "    print \"BEST\", gs.best_params_, gs.best_score_, gs.grid_scores_\n",
    "    best = gs.best_estimator_\n",
    "    return best\n",
    "def do_classify(clf, parameters, indf, featurenames, targetname, target1val, mask=None, reuse_split=None, score_func=None, n_folds=5, n_jobs=1):\n",
    "    subdf=indf[featurenames]\n",
    "    X=subdf.values\n",
    "    y=(indf[targetname].values==target1val)*1\n",
    "    if mask !=None:\n",
    "        print \"using mask\"\n",
    "        Xtrain, Xtest, ytrain, ytest = X[mask], X[~mask], y[mask], y[~mask]\n",
    "    if reuse_split !=None:\n",
    "        print \"using reuse split\"\n",
    "        Xtrain, Xtest, ytrain, ytest = reuse_split['Xtrain'], reuse_split['Xtest'], reuse_split['ytrain'], reuse_split['ytest']\n",
    "    if parameters:\n",
    "        clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds, score_func=score_func)\n",
    "    clf=clf.fit(Xtrain, ytrain)\n",
    "    training_accuracy = clf.score(Xtrain, ytrain)\n",
    "    test_accuracy = clf.score(Xtest, ytest)\n",
    "    print \"############# based on standard predict ################\"\n",
    "    print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n",
    "    print \"Accuracy on test data:     %0.2f\" % (test_accuracy)\n",
    "    print confusion_matrix(ytest, clf.predict(Xtest))\n",
    "    print \"########################################################\"\n",
    "    return clf, Xtrain, ytrain, Xtest, ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "def points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=True, colorscale=cmap_light, cdiscrete=cmap_bold, alpha=0.3, psize=10, zfunc=False):\n",
    "    h = .02\n",
    "    X=np.concatenate((Xtr, Xte))\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
    "                         np.linspace(y_min, y_max, 100))\n",
    "\n",
    "    #plt.figure(figsize=(10,6))\n",
    "    if mesh:\n",
    "        if zfunc:\n",
    "            p0 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0]\n",
    "            p1 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "            Z=zfunc(p0, p1)\n",
    "        else:\n",
    "            Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "        Z = Z.reshape(xx.shape)\n",
    "        plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=alpha, axes=ax)\n",
    "    ax.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cmap_bold, s=psize, alpha=alpha,edgecolor=\"k\")\n",
    "    # and testing points\n",
    "    yact=clf.predict(Xte)\n",
    "    ax.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cmap_bold, alpha=alpha, marker=\"s\", s=psize+10)\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    return ax,xx,yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def points_plot_prob(ax, Xtr, Xte, ytr, yte, clf, colorscale=cmap_light, cdiscrete=cmap_bold, ccolor=cm, psize=10, alpha=0.1, prob=True):\n",
    "    ax,xx,yy = points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=False, colorscale=colorscale, cdiscrete=cdiscrete, psize=psize, alpha=alpha) \n",
    "    if prob:\n",
    "        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "    else:\n",
    "        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=ccolor, alpha=.2, axes=ax)\n",
    "    cs2 = plt.contour(xx, yy, Z, cmap=ccolor, alpha=.6, axes=ax)\n",
    "    plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14, axes=ax)\n",
    "    return ax "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Setting up the data\n",
    "\n",
    "(I encountered this dataset in Conway, Drew, and John White. Machine learning for hackers. \" O'Reilly Media, Inc.\", 2012.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfhw=pd.read_csv(\"https://dl.dropboxusercontent.com/u/75194/stats/data/01_heights_weights_genders.csv\")\n",
    "print dfhw.shape\n",
    "dfhw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sample 500 points from 10,000, since we actually want to see trends clearly on the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=dfhw.sample(500, replace=False)\n",
    "np.sum(df.Gender==\"Male\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into training and test sets..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True, False,  True, False, False,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itrain, itest = train_test_split(xrange(df.shape[0]), train_size=0.6)\n",
    "mask=np.ones(df.shape[0], dtype='int')\n",
    "mask[itrain]=1\n",
    "mask[itest]=0\n",
    "mask = (mask==1)\n",
    "mask[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Logistic regression\n",
    "\n",
    "Notice that its L2 regularized...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using mask\n",
      "BEST {'C': 0.1} 0.926666666667 [mean: 0.91000, std: 0.03397, params: {'C': 0.001}, mean: 0.92333, std: 0.02874, params: {'C': 0.01}, mean: 0.92667, std: 0.02648, params: {'C': 0.1}, mean: 0.92333, std: 0.03189, params: {'C': 1}, mean: 0.92333, std: 0.03189, params: {'C': 10}, mean: 0.92333, std: 0.03189, params: {'C': 100}, mean: 0.92333, std: 0.03189, params: {'C': 1000}, mean: 0.92333, std: 0.03189, params: {'C': 10000}]\n",
      "############# based on standard predict ################\n",
      "Accuracy on training data: 0.93\n",
      "Accuracy on test data:     0.94\n",
      "[[ 84   8]\n",
      " [  5 103]]\n",
      "########################################################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
     ]
    },
    {
     "data": {
      "image/png": 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5nR4sLJT/hkW016VoebSoKVqeSnQ56Gcjr35qMo94NZlHvJrMU/pYIBDC++8v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zZLKp8nF49jOMQxC0ejZG/dgtZWoNAbQY7FkL/9bfw+H958HsVg2Ga4fvLxXi7KcnmzUdndlLcb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae1f490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clflog = LogisticRegression()\n",
    "parameters = {\"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000]}\n",
    "clflog, Xtrain, ytrain, Xtest, ytest=do_classify(clflog, parameters, df, ['Height','Weight'],'Gender', \"Male\", mask=mask)\n",
    "Xtr=np.concatenate((Xtrain, Xtest))\n",
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "points_plot(ax, Xtrain, Xtest, ytrain, ytest, clflog);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `sklearn`, `clf.predict(test_data)` makes predictions on the assumption that a 0.5 probability threshold is the appropriate thing to do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clflog.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probabilities output by the classifier can be accessed as below. The second column (`[:,1]` in numpy parlance, google numpy indexing to understand the syntax) gives the probability that the sample is a 1 (or +ive), here Male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.02321126e-01,   1.97678874e-01],\n",
       "       [  1.11597729e-02,   9.88840227e-01],\n",
       "       [  9.96288121e-03,   9.90037119e-01],\n",
       "       [  2.62317652e-02,   9.73768235e-01],\n",
       "       [  1.19335482e-01,   8.80664518e-01],\n",
       "       [  8.50036692e-01,   1.49963308e-01],\n",
       "       [  9.97672775e-01,   2.32722532e-03],\n",
       "       [  8.09749102e-03,   9.91902509e-01],\n",
       "       [  1.18457126e-01,   8.81542874e-01],\n",
       "       [  9.92730072e-01,   7.26992825e-03],\n",
       "       [  9.90508354e-01,   9.49164621e-03],\n",
       "       [  8.58114319e-02,   9.14188568e-01],\n",
       "       [  1.79740323e-03,   9.98202597e-01],\n",
       "       [  9.54249098e-01,   4.57509020e-02],\n",
       "       [  8.35500442e-05,   9.99916450e-01],\n",
       "       [  5.06121153e-02,   9.49387885e-01],\n",
       "       [  9.49267296e-01,   5.07327042e-02],\n",
       "       [  1.22369365e-01,   8.77630635e-01],\n",
       "       [  9.85718527e-02,   9.01428147e-01],\n",
       "       [  3.57816309e-02,   9.64218369e-01],\n",
       "       [  9.87899084e-01,   1.21009163e-02],\n",
       "       [  9.95337064e-01,   4.66293644e-03],\n",
       "       [  2.19696652e-02,   9.78030335e-01],\n",
       "       [  9.97801262e-01,   2.19873822e-03],\n",
       "       [  9.73122592e-01,   2.68774082e-02],\n",
       "       [  9.99599140e-01,   4.00859833e-04],\n",
       "       [  2.44168919e-03,   9.97558311e-01],\n",
       "       [  4.82032966e-01,   5.17967034e-01],\n",
       "       [  9.90765398e-01,   9.23460180e-03],\n",
       "       [  9.99812233e-01,   1.87766625e-04],\n",
       "       [  1.34839196e-02,   9.86516080e-01],\n",
       "       [  8.47489212e-01,   1.52510788e-01],\n",
       "       [  8.14181482e-04,   9.99185819e-01],\n",
       "       [  9.66314197e-01,   3.36858026e-02],\n",
       "       [  1.98985610e-01,   8.01014390e-01],\n",
       "       [  9.99236831e-01,   7.63169498e-04],\n",
       "       [  9.73667211e-01,   2.63327895e-02],\n",
       "       [  2.44412131e-03,   9.97555879e-01],\n",
       "       [  3.68464644e-05,   9.99963154e-01],\n",
       "       [  2.50988306e-04,   9.99749012e-01],\n",
       "       [  9.97574494e-01,   2.42550597e-03],\n",
       "       [  1.39667289e-02,   9.86033271e-01],\n",
       "       [  9.59216694e-01,   4.07833056e-02],\n",
       "       [  9.22727432e-01,   7.72725684e-02],\n",
       "       [  7.55505079e-02,   9.24449492e-01],\n",
       "       [  8.45127518e-01,   1.54872482e-01],\n",
       "       [  9.21088760e-01,   7.89112398e-02],\n",
       "       [  9.03864461e-01,   9.61355388e-02],\n",
       "       [  1.79453945e-02,   9.82054606e-01],\n",
       "       [  1.14736131e-02,   9.88526387e-01],\n",
       "       [  9.99971826e-01,   2.81737579e-05],\n",
       "       [  1.39343622e-04,   9.99860656e-01],\n",
       "       [  9.95787076e-01,   4.21292380e-03],\n",
       "       [  9.99023382e-01,   9.76618292e-04],\n",
       "       [  9.99806196e-01,   1.93803993e-04],\n",
       "       [  1.82538766e-01,   8.17461234e-01],\n",
       "       [  3.34843772e-06,   9.99996652e-01],\n",
       "       [  2.15435249e-02,   9.78456475e-01],\n",
       "       [  2.56369703e-01,   7.43630297e-01],\n",
       "       [  4.25397208e-01,   5.74602792e-01],\n",
       "       [  9.75370070e-01,   2.46299298e-02],\n",
       "       [  9.98951796e-01,   1.04820365e-03],\n",
       "       [  1.18312809e-02,   9.88168719e-01],\n",
       "       [  3.61553758e-01,   6.38446242e-01],\n",
       "       [  6.67596112e-04,   9.99332404e-01],\n",
       "       [  6.28097920e-03,   9.93719021e-01],\n",
       "       [  3.59679316e-01,   6.40320684e-01],\n",
       "       [  9.99973313e-01,   2.66871575e-05],\n",
       "       [  3.23441438e-03,   9.96765586e-01],\n",
       "       [  2.31822323e-01,   7.68177677e-01],\n",
       "       [  4.13104381e-04,   9.99586896e-01],\n",
       "       [  7.90607857e-01,   2.09392143e-01],\n",
       "       [  9.65037774e-01,   3.49622260e-02],\n",
       "       [  9.96501200e-01,   3.49880045e-03],\n",
       "       [  3.67245818e-01,   6.32754182e-01],\n",
       "       [  8.72024676e-01,   1.27975324e-01],\n",
       "       [  1.55943446e-01,   8.44056554e-01],\n",
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       "       [  9.84425582e-01,   1.55744181e-02]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clflog.predict_proba(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "         9.97663108e-01,   1.55744181e-02])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clflog.predict_proba(Xtest)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of sure females and sure males when you plot the probability of being a male:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LF2bevHlZunRpdu7cedwGBgAARm/IALj77rvz+c9/Ppdddlk6Ojpy8cUX59Of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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ed7d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(clflog.predict_proba(Xtest)[:,1]*100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the probability contours: these are rather tight!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3SzV44nA3Hc2KQAMQtw9q8fOZJNZXs1hfzeJ6MY9rxTyso+PDL9ju3V8Mfd+p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\nZJiFZJhyU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AGAFg4jhaPIsky4VCJV9ibaZhaL5C8sos4YSmdZtt/8u1vptx3HawDe8x6f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\nO2jO6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db2fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "points_plot(ax, Xtrain, Xtest, ytrain, ytest, clflog, mesh=False, alpha=0.001);\n",
    "points_plot_prob(ax, Xtrain, Xtest, ytrain, ytest, clflog);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score function of the estimator is used to evaluate a parameter setting. These are the sklearn.metrics.accuracy_score for classification and sklearn.metrics.r2_score for regression. For some applications, other scoring functions are better suited (for example in unbalanced classification, the accuracy score is often uninformative). We can pass other scorers to `GridSearchCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91649999999999998"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clflog.score(Xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##SVM\n",
    "\n",
    "Lets do a linear SVM instead. As mentioned in the last lab, SVM is a discriminant classifier..it is not probabilistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using mask\n",
      "BEST {'C': 10} 0.933333333333 [mean: 0.91000, std: 0.05898, params: {'C': 0.001}, mean: 0.91000, std: 0.05560, params: {'C': 0.01}, mean: 0.92667, std: 0.04341, params: {'C': 0.1}, mean: 0.92667, std: 0.03356, params: {'C': 1}, mean: 0.93333, std: 0.03283, params: {'C': 10}, mean: 0.92000, std: 0.03902, params: {'C': 100}, mean: 0.91667, std: 0.02692, params: {'C': 1000}]\n",
      "############# based on standard predict ################\n",
      "Accuracy on training data: 0.93\n",
      "Accuracy on test data:     0.93\n",
      "[[ 84   8]\n",
      " [  7 101]]\n",
      "########################################################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
     ]
    },
    {
     "data": {
      "image/png": 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5nR4sLJT/hkW016VoebSoKVqeSnQ56Gcjr35qMo94NZlHvJrMU/pYIBDC++8v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BL5e98jHAoRLJVQLJYthutnn+7joixXNxtV3U15q2FX1xs2T1disVhwu1zGciGBQCAQCOrQkkm/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b000390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clfsvm = SVC(kernel=\"linear\")\n",
    "parameters = {\"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "clfsvm, Xtrain, ytrain, Xtest, ytest=do_classify(clfsvm, parameters, df, ['Height','Weight'],'Gender', \"Male\", mask=mask)\n",
    "Xtr=np.concatenate((Xtrain, Xtest))\n",
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This there is no `predict_proba`. Instead, we get to use `decision_function`, which give us whatever the said classifier is using to discriminate..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.02394603,  3.04069287,  2.99604368,  2.35411247,  1.37973225,\n",
       "       -1.21382287, -4.1013604 ,  3.08972663,  1.26559889, -3.40543771,\n",
       "       -3.16196104,  1.48724872,  4.08735164, -2.25340012,  6.25924854,\n",
       "        2.03970076, -1.99599323,  1.29175595,  1.31935835,  2.14863131,\n",
       "       -3.11073689, -3.65382496,  2.53618263, -4.15337139, -2.3521498 ,\n",
       "       -5.25768266,  4.01121459, -0.16207572, -3.15439156, -5.74203179,\n",
       "        2.73611149, -1.24596257,  4.56577756, -2.41416596,  0.667481  ,\n",
       "       -4.91485032, -2.37529785,  3.85092495,  6.81826616,  5.48143975,\n",
       "       -3.96856438,  2.86347904, -2.20194496, -1.65950846,  1.61902128,\n",
       "       -1.06773245, -1.63326733, -1.59990362,  2.59784562,  2.95506673,\n",
       "       -7.12619344,  5.91268736, -3.73104009, -4.7026291 , -5.81594601,\n",
       "        1.01654758,  8.35614995,  2.56870653,  0.64048269,  0.14001682,\n",
       "       -2.54792884, -4.72348078,  2.93087024,  0.32357343,  4.91027965,\n",
       "        3.39013864,  0.30813327, -7.18335067,  3.80506741,  0.696064  ,\n",
       "        5.29279532, -0.92885383, -2.37750652, -3.75757216,  0.27978961,\n",
       "       -1.38905424,  1.04684899,  5.35002699, -3.38986592, -2.89305219,\n",
       "       -3.27229697,  5.5873905 ,  4.59843736,  2.98558146,  5.25591072,\n",
       "       -2.24681531,  3.87358381,  2.58546965, -4.19005223,  0.79411307,\n",
       "       -1.40535115, -1.19346184,  4.32101692, -2.09385453,  1.90133321,\n",
       "       -3.4656755 , -3.10200974, -6.24591448, -3.85426652,  3.61513713,\n",
       "       -4.23995207,  1.07105358, -0.36249753, -0.87594756,  4.7222594 ,\n",
       "       -0.09694593,  4.89455069, -3.75789224,  3.94491104,  0.06584362,\n",
       "       -1.19674532,  5.28204344, -3.31905568, -2.68951747, -7.11927877,\n",
       "        0.3979681 ,  0.5361304 , -2.34538524,  6.95607864,  2.06173033,\n",
       "       -1.137308  ,  2.4192738 ,  3.66202656, -4.71119817,  0.57496873,\n",
       "       -3.61035391,  3.68581235,  3.56797406,  3.01897161, -0.35569789,\n",
       "       -0.12722463,  0.97103921,  7.73797492, -3.82834457,  2.75863056,\n",
       "       -6.26735199,  0.01962214,  1.59978319, -3.34311841, -5.06098589,\n",
       "        2.70812601, -3.51027846,  9.07904345,  5.01041067,  1.46305792,\n",
       "        0.811446  , -2.67857802,  4.21358136,  1.41817337, -5.10414657,\n",
       "        0.0474447 ,  1.69868508, -2.11461398, -2.35492993,  0.66290719,\n",
       "       -3.84691069,  2.80278015, -4.22594901, -1.84937925, -2.32558735,\n",
       "        2.77849317,  1.49378476,  3.14341949,  1.96073033, -5.3649383 ,\n",
       "        3.81266098,  2.31958616, -5.42198702, -3.17289797,  0.44716446,\n",
       "        4.71999693,  4.6195511 , -1.78088737,  3.17345105, -1.6162107 ,\n",
       "        4.69317468,  5.07280776,  3.84577724, -0.20347673, -2.43028451,\n",
       "       -2.98299014,  4.57084438,  0.19470207, -6.31595014,  6.92256866,\n",
       "        4.443784  ,  1.97663482, -3.30502991,  1.24278158,  2.89478121,\n",
       "       -2.87553433,  2.61004278, -0.62803545,  2.19396339, -1.19188281,\n",
       "       -5.86406824,  4.00333806,  2.23738927,  4.12639375, -2.79317256])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfsvm.decision_function(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plt a histogram. In terms of distance from the separator, we can see that most samples on either side are a middling distance away. This is different from the probability diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kq6urS//+/fdZ76yvuUcie9d+e/euvTr8DP/AgQNTKpWyYcOGFusbNmzIoEGD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\nX+kZN7p02VVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ccd1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(clfsvm.decision_function(Xtest));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its clearer when u plot the `decision_function` on the plane..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AnZDvk622266QB7GgipCHRVhERLSyGPqJiOaQLZQQTxWcoD+Zq992G1QldIUq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\nYDCw4fNIJJZ4551H5HIaFy746Ok5uuY2Fy50MjMzQCKhce6sE4eug65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53x8mHo8QWq6r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3/6U3K5HE899RR/9Ed/RGNj4yaHLxAIVsUwyiU61TCstUS+tLgeX1LAYsHZe5q+G1+Az0fnpUvb\nOuSltfFr1cUrVoXuV18lm8nS43Fz75NrjA4PkcHCdKCR2M07NDeHaGjwLpo8HDl1Er2nG8ki1dU4\nms1mufXrD3D4fBzu7AAWT0aCgSDJF7+J1W7HVynx8QGqsXaBTzAQhNdfwwvc/clPOWUt35anhoah\ns7PmvqlYnPTEBM0nunA4HoZnAwKBQLAYo1RCjUfn026TibXTbiv1+NZQBLnBtyMe+XuBukX/Z599\nxh/+4R8ue72vrw+n08mf//mfL3p9oaD/4z/+Y95++23+9b/+1zidTn7wgx/we7/3e/zVX/0V0gGd\nTQkEO87CtNtqEFYNkV8Nwppz1pFXLNeJtLQQaWnZ9uHPsbQ2fi3ZLMtytfmz64nHyZ85w+jndxj/\ny6tYNIVJ+zUijxxeNnlYjz3kF//vX9HyxRcgSSRbWrB5vYuOZ5Es+AMBZgBtwX7pXJaGNYT/QivN\nyYCfgAlFXUPx1n6ikpiaIvfOO7TY7AwODtL92qvC8lIgEBx4jGKhUo8/hRqbRE2naqfdOpwLnHUi\nyB7vQyPyl7Km6C+VSvz5n/85f/qnf4rL5UJV1UXv37lzh+7ubs6cObPi/iMjI/z4xz/m3/27f8fL\nL78MQE9PDy+99BJvvfUW3/jGN7bgMgSCh5A6024XBWEt9chfcuPbKc/47cTpcpJIqvg9TaDqaFqh\n6oaz3vIhKvvYSyW8dhsBu4MxQ8e5grtOOpVC+/ImlgVivTSbIf3MFSKRcM0xz07PMPr3f48ln+c9\nw6D11Ck618giSI9P0O5wIssSvmyWYqGISzTCCgSCA4aey87X40en0GbSQI20W5enknZbrsmXXO6H\nVuQvZU3R/9577/Ff/st/4V/+y39JKpXiRz/60aL379y5w4kTq3tnf/jhhwA8++yz1dfa2tro7Ozk\n/fffF6JfIKiXhR756tal3S5kL3jGz+YLi/69kQ6C9rYGRt6Ngmol6J9fqKinfKhULHHv6lXQddqe\neAKn24XR1ka0r48ZRaGptZWcpi07J4BsWXxTlev8OzN+/TonbVawWekvFOi4eGHNfZpPdNE/OIC/\nYDAdCtPiEoJfIBDsf/Rsplqqo0Yn0TKzQI0gLI93vuk2FEF2invhaqwp+k+fPs3bb7+Nx+PhP/yH\n/7Ds/f7+fux2O6+//jqDg4McPnyYf/pP/ymvv/46AENDQ0QikWX1pkeOHGFoaGiLLkMg2HpUVUVR\nlN1ZIViWdlsOwoKtT7tdymasJzdLMBCE116tns8z99o6OdwSxt5poZBJ43TYF00e1iofuvfBB3RN\nTyNLErfff4/el17i2IXzTN8fxuNyk9O0FScj/kCA7COPYC44ti2TwR0IrDlem9/PbCyKS7GSKJZI\nJBKL3l/paYTL7abrjTfI5ws0ezybzhqY4yA87REIBPsD0zTRZ2cWp93msqvbZwJyJQirmnZrF/1M\n9bKm6G9qalr1vampKdLpNCMjI/zzf/7PaWho4G//9m/5V//qXwHw+uuvk81mca2wAuVyuZicnNzQ\noMVkYf9TLBaBvfm9NE2Tjz4aJplswOGY5etfb8FqtW73SZExsKIjmxo2i4GEiWTqyzY19PJrmmlB\nNSxoJqimBXMLVF86lcKTSKAvWG1P57Jk7o8wMzO75v5jX32FlEoiHWqmaY0m1Hqp57xL0XWNse5u\nSpWfM5vdzpFUitmZ2UXXt9K1RcceECqVnw7EgaGhYUzDYPrCRaYXniM9vWi/dCqFp1ikKM/fVtVi\nkQejo2QyK/wBW4jXy5cNPpITE0Qmxkn+xV9U38oUCow89zz+GpOHeDy+1kdSN+lUirfeBodjfvJS\nKGR4/rmRmmN4WCkWyz9LQ0PDuzsQwYHmoPycmaYJmVnMdAIzlcRMJ6BYhGJ+2bYGUrkE1e2BhgA0\n+KHBV+5DAyhpMDGxw1ewD/Csfp/elGWn3+/nz/7szzhx4kT18fjly5eJRqP8x//4H3n99dcxTXPV\nlVLRxCvYi2QyGRLJRlzORkpqgZHRB3Qcb97akywQ+XP/WTCZTWXIFSxIkkEo7ABJWibyVVNC2yKR\nvxKZQqHm1ysxde8escFBDqdSHG1sZPxOH5lDh/DUmZC7FrGREUrDwxgeD63nzq1ppzY7M8unn/pQ\nFCcAmpbH76tv8hA4fYZb164hmyaeSq+SRZLqErwb+ezKJ7DQ3NWFMxzGMzuD3+VefJz6jrJlOBwe\n3C7/kld3ehQCgWC/Y5oGzM5gphKY6SRmKgGqurrIlyzgaQCvH3x+8PhA5HdsGZv6JO12O5cvX172\n+lNPPcX7779PLpfD4/GQzWaXbZPNZvF6N+bNfOzYsQ3tJ9g7zK3w78XvZbFU4vbtIVyuRjKZUc6e\n6SESWX+JySKqabf5eYcd04Kp6YAMyBiGwbThQpGd6IbJ9OwMR9qb5st1dqDMyGxrI9l2dNFrQWqX\ndkTHxuiIxzlut5NIJvGfOIHmdOA+erTqqrMZNFWj9MEHdPp8FHWdaD5H+yOP1Nwn0eClpcVCIV+e\nMDmcMkfbyoU8mevXkSs1/TbgaNvR5f0KZ+eNCeotd9nIZ7fSuC13+haVH8kZB8GVxrgFrHRtmqoS\nDATxNsyfz5GROdoW3Pa+DtMwmZ6ZxuP2oFj3xx/6uZXXY8fad3EUgoPOfvk5M3UdNZlAjZVLdUqx\nKKa2JO1WtoDLVQnCklECoaqFphIIHZggrN3iq3h61fc2dVcdGhri6tWrfPe738Vms1VfLxaLOJ1O\nXC4X7e3txONxSqXSom0ePHjAo48+upnTCwTbgt1m4xvfOER//x3OnfNvTPCbRkXk1067hfkgLIti\nY2gijWocJpWe5uIlDzjWt1J+584o0ak8J081EgotXaldm4X2kfVSmJ0lIss4Q0Hu+Xzc1jU8Pb00\nb4HgB/jq1ghfXZ8lai9wvF3B3ESDwUZ6Buptbt7IZ7cSCxuZ577erji0la6tGI+TdXxrkejfCUzD\n5NZPf0pjZpYBq41j3/6WyB4QCPY41bTb6CSl6BRqIoqp1wjCUhSUYHg+DMsXECJ/B9mU6J+cnOTf\n/tt/S2NjIy+88AJQrtd68803uXjxIlAu99F1nbfeeqtq2Tk8PMzg4CB/8Ad/sMnhCwTbQzDo44kn\nfPXvsJG020XOOgqSxcLZR218dfM+R9udHDveuq4xDw2N88UNFw7nEd5+q483fsODsgOPRVu6urhz\nfwTr7Ayh556j6/ITW3r8oSEN/cQLjE7d5UEmyT86dWrDx7r76aeYo6NoDV5OPPts3b72O9XcvHRS\nAhtvZq6Xpdc2k8lQLGbIZOafABTys5WRbB/TM9M0ZmaJuN14iiVi9+9zpLt7W88pEAjWh6GWUOOx\nctJtbAo1Gcc0agRhWW0Lmm4by0FYorR719iUInj88cc5f/48f/zHf8z09DThcJi//Mu/ZGBggL+o\nNKIdPXqUl156iX/zb/4NmUwGr9fLD37wA3p6eqoTBYFg31FNu533yDdNk0x0EkolLE4nbl950lAr\n7XYpPp+XJ7+2sbK3dDqP1VYOxdMNN5qm7Yjol2WZk998cduO7/HowHHMUDNHjo7XXSpTyM+Szc1N\numzMzpo4Bgdo9XjITc8wPniXI92r2w2vh61yvNmqpwWbwety881nXARDi6ce2znxAPC4PQwoCt6S\nyphaounQoW09n0AgWBujVKQUi5bLdaJTqKlE7SAsuwMlFKmW68jeBuGRv4dYlyKwWCyLvnmSJPGf\n/tN/4gc/+AF/+qd/Sjqd5tSpU/zoRz/i5MmT1e3+5E/+hD/5kz/h+9//PoZh8OSTT/JHf/RH4gdB\nsH9YFIRVXDHtNp/LYc/nsWkaM+lpCh4/To+nZtrtVnLq5BHGxvrJ52wcazcPTGnElStdfPnlfRx2\nhe7u48vsLGG5uA4Ggrz6GozcHwHKdfsup4sJS3mFKadpWLfw89kL+QYbZWk5UaZYJBja/vr9pShW\nhWOvvEJ0eJim5ma8vnU8aRMIBFuCUciXa/Gj5TAsNV1ezFhV5DtdZYFfEfmS2yO03R7GYpqbqZDd\neT799FPOr5FUKdj77OVGXmA+CEtbKPJrB2Gl0zNIN2/TgIXR9DTBV1/F7V7swiJYgAkDH36ImU7h\nOXGCwx0da+6SSCRWFNeeVcT10ua3qfsjzNy7hy0Spm2NZuD1nDORSGB5881FZTLJTAbzxRf3tOgX\nnvybZ780WAr2N9v1c6bnshWBX17J12bLxsSrBmG5vSih+Zp82SX+xu01voqnqyX2S9kf9ggCwXaj\nq4tr8utNu10QhOX3hhmcSjIVi+E4d14I/hXIZXMUSyUCAT+j/f0cGnuA125n6JNrFFtbsdvtax5j\nM/X1TW1HaVrisLMWu1Fnv1PshXIigUCwM5imiZ7NVEOwStFJ9Iq4X1Xke30LVvLDSA7nTg9bsIUI\n0S94+DDN8kq+VoBSZTVf11ZvuoVK2q1tvi5/lUakzkuXtm/c+5zo6AOyv/4VbtOkr6UVZySCbJn7\nHE326kPHeoXxTrruCAQCwVqYpok+M12xzpxEjU6h53Orp91aLCiVtFslGMEaCiPZ1l6IEewfhOgX\nHHxMs7KSv6Dx1tDXEPnWxY23lu1zGygUCqiqhtd7sCXi9NAQXZWnH9PRKY4+9RT9sRjSzDSOM2fr\n7kHYi+L6ID8NEAgE+wPTNNHSqaqzTik2hVEs1BD5Eoo/MO+RHwwhWW3LtxUcGIToFxw8qkFYhXmH\nHaOWRz7lG2B1JX97Rf5C7t+f5OrVHKZp40TXFBcvrV3Xvl9xHW5mYnwcjyyhBQJYJAvdT399XcfY\nq+J6K8pkTMNk6MsvMQ2D9jOn67YTjY6MkvrySwy7na5nntk3oVYCgWBzmIaBlkpUS3XUWBRDLa0u\n8iUZayCENRQur+QHQlhE2u1DhfhuC/Y/pln2xV9HEFZZ5Ft3NO12JQYHZnG7uwAYGR3k4gGuDmrp\n7CTl8zObzdBztG1DxzgINegrNc8GA0EGr16ldWoKGehPp+l99kpdx0t+/BE9djtaJsPQp5/S9cTj\nWz9ogUCw65iGvsBZZ5JSPLY87XYO2YpFVlCCofkgLH9QBGE95AjRL9h/bCQIa6HA30WRv5TGRit9\nfQlk2UkgoO32cLYETdVWXW0ORMIEIuEdHtHusJq4X2rvOWftaczO4Kx8bnJmdo2DQzwex263L+iF\nMPfMz7VAINg8hqqiJmKosSn0vtuY0ymSHneNICzrvMAPRlB8fhGEJViEEP2CvU81CKtYp8ivLwhr\nL3D6TDs+3ySFQo6urv2dPqrrOn2/+AXu9DRZv5+TL774UNs+ribuYbkDkQn4e0/S/9FHWEwT98UL\nNY/d9957hMfHSAP6sWP0JVPgbaBzjf0EAsHexVBLqLFopfF2Ci0RxzTLabdmLlfexlKZ5MtWLDZ7\ntR7fGqoEYQmRL6iBEP2CvYehL1nJXx6ENYcFyk461ZX8nQnC2kqOth2M5NH45BQt0zP43S5S09PE\nJidpPNy828PaVVYS96vR1HaUcGsLwJr1/HIsSrhy3EKhQNe3Xt70WAUCwc5iFItlV525mvx0atW0\nWwMJbHbsRzuqzjqyR6TdCtaHEP2C3WdR2m1h1SAsWCjyF3vk7yeRv15WKxPZa6voHr+PqKHjB9Km\nQaOvYbeHtCPMJFNEh4YIt7fh32S/Qb3Nu1o4TGoqStbQcS9IPxcIBHsXPZ8rr+RXfPK16RSwuke+\n5PJgDYWxhhqZzebA7sB77PhOD1twgBCiX7Dz6Bo2U8WKDskH9Qdhza3mSw9XI9JqZSJ7raHV7XbT\n8OyzDAwNE3zssYcinCyfyzP187/juNPFUP8dbK++hsvtqr6/0F50obXoaq/XS+8zV5gaH8fjcuEP\n+DdxBQKBYLvQs5mqwFdjU2izM0CNICyPd74mPxRBds7fSxge2qlhCw4wQvQLthfTBENbvJKva3gp\nIJk6ZnG+/nBxEJZ18Ur+Q856ykR2k9ChQ4QO7b9yJV3XuXf33qLX/IEA4VCo5p+6CsYAACAASURB\nVBOV2ZlpIhYJWZaIWCRmUsmq6F9qL7rIWnS11+vFAk0th9e3j0Ag2DZM00TPzJYFfnSSUnQKPbdG\n2m0lCMsaDGMNRZDs9WWVCAQbRYh+wdayLAirCMbytFvJ1DF0vSz0ZWWBs87qabeC/cPo7T4K9+5h\nNDTQ/dRTC2Z0e5N7d++R+ff/nnBFsOdVlanOTqTf/u2aT1RCkQh9HjfZTIa0y033gglPLXvRvfaU\nRiAQrI9y2m2aUnSquppvFPK10259AazBMEql+VayiSAswc4iRP9DwtTUFNlsluPHt7ge0DRBLy1u\nvK0j7TZbMlFNiYaGyI4FYe13TBMe3OljNpVGO9pG6GtPbu3xV+gdgPX3DxQKBfQbN+hyu8hMTTLa\nf4cj3Ss7E8XGxkh8cg1TkTl65QpuT32FLls11oWE3S6O+HwAzBYKJOpICJZlmVPffoVcLsdhp3PP\n9VkIBIKtwTSMctptNQhrCqNUrBGEJaH4g5VynTBKIIxkte78wAWCBQjRf8BJpVL88Ic/ZHJykkAg\nQGtrK7/zO7+z8Y7/hWm3c//VDMKivHpfLddRwCKRi81UNhKCvx5m8wViE5P4Egm8ukHi9i2Kly5i\nt9vXfaxa/vF//eMMDqe3+nohP8urr61vZdpisbDAOZ656d7Y4CCz169jWm0cff453B4PiU+u0SNL\nYJj0ffQxPc8/V9c5lvY5wC72OlhYVMcvEAj2P6ZhoCbjFWedKdT4FIZaKwhLRgmEqhaaSiAkgrAE\new4h+g84P/7xj/H5fPz+7/8+0WiUH/7wh/z0pz/l+eefx1HHSiamsUTk15l2uweDsPYrc7Xh0wOD\nlPpu47HbKQCWDdbM1PKPdzi9eDxL68vX10Fgt9uxXrzIwOAAhCN0nTgBQOb6DXrsdjCh79o1eq5c\nwVRkMEw0w8CyzlWwpX0O6x/pYuLZXPXfeVWlVCjgrbF9LUzDZOjGDfRCgaMXzm9ociYQCHYOU9dQ\nE/H5xtt4FFPXVxf5ioISDM833voCQuQL9jxC9O9T0uk077//PgMDA3R3d/P000/j9c5LFNM0MU2T\noaEhXnrpJRoaGmhoaOCVV17h888/p729nVOnTmGa5uqr/vkZyCbrSLu1zYdhyXs3CGu/MlcbHgqG\nGLBYKKSSeLtOYLNvvB50KxuDV3py0NLRgaWrc9FrhtUKJhRUFdlVdvY5euUKfR9+hMVmo+Py5U2M\nYjmj/QM8+OIL/EvGsRLHO45z75/9M+Y8dSxAUyCw/gbbCoMff0zr+Dg2SeLOW29zUvjoCwR7irm0\n27lSHTURwzSMGmm3tmrTrRKKoDSItFvB/kOI/n3Kz3/+c0ZGRjhz5gwfffQRIyMjvP766zQ3N2MY\nBpIkMTY2hs/nwzCM6n5dXV189dVX9PX1cerUqVUFv5GbBrWIRS1WX9tvabfbgWmYDH70EcbsLKHT\npwk376BTjQW6Hn9s585XJ/Vaira98Dx3rn2K5HLRefEiAG6Ph54Xnt/QeRfaXs59PTeNmRgexnn9\nOj2ZDH0ffID+yCM1PfBlWabrRNeGxrESemYWp7V8e5WLhTW2FggE241RKlKKRVFjZWcdNZVYNQgL\n2Ypkd5RX8kNzabc+EYQl2PcI0b8PGRgY4Pbt27z++uucO3eOnp4efvazn/Hmm2/yve99ryr6HQ4H\nsiyTz+er+0YiEcLhMGNjY4uOaeSml51HUpSyXaZsnRf6BzwIay2GbtygdWICp1Xh9vvvE/6t39rz\nzjTroZCfXeHr5c21C1f3k4kkkq4BEHCXt13pyYHL7ab7mae3ZJxL7TBhsfVlNpHkmM1GEvAYJqqq\n1h18tRUcOn+e2798F0nXcZ09u2PnFQgEZYxCnlIlCEuNTaGmy/erVUW+01Ut1bGGIkhujxD5ggOH\nEP37kGw2Sz6f59y5cwC0tbVx9uxZ/vt//+9873vfQ1HK39ZwOIwsy0xPT6PrOrIsoygK4XCY4YE+\nYqNDhEJlkSStJNMsEnjDO3Zd+wFdVbFWHunKlRKq/fiHYaVwqGAgyKuvAUuk9EolLgtX962ZDPKd\nO2QtEub58xiGTr0PvQuFAsMffojFItH+xON1177XssMEOPLIKfoePCCTz1NqOVxf/8oKbDQN2R8K\n4f/N3yh/lEs23Q7nIYHgYUfPZashWKXoJNpMeSFrVY98t6dqnWkNRaolhwLBQUaI/n2I0+kkm80u\neu3kyZNomkZ/fz8nTpyorvaHw2Hi8TiJsfuEw2WR5LZasFmtpFNJIqHAblzCvuXY+fMMpNPI+Tzu\nixe2VKTtlBhcLTRqLSG9lLm+AAWw2O3kVY3xL25g0w1mIo2EvvnimscYeu99egoFDMOg//336X3h\nhQ1d01LsdjunXnuVe3fvbarudmnpUjSVJv7ss3T39NT3hGeFbbbKeSgxNUV6fIJDXZ11W50KBAcB\n0zTRs5mqwC/FptAz5aeUq4p8b8N8020wgux07vSwBYJdR4j+fUhDQwMul4v79+/T1taGYRj4fD6a\nm5vp7++ns7UJXddBljneeohfjQwxOjpCY7i8Yhv0NTAzO4vLJW5660WxKvS++I1tOfZO2VCuV9zX\nQ65YIppI4AkFUSwSudHR6tMlgFQ0RvSLL5D9PjovXqqKYYuqlnvBZQk0re7zLZwgGYZJOpUCyim6\nkmSpTmLWK/iXTrzmSpcCbg/TySTSvbvINiu3x8fprdiL3rgxzMSESnOzlbNn2+s6z2adh1LxONl3\n3qHd4aR/cICu199AsYrbueBgYpom+ux0OQgrNokanULP51b3yAcUX2C+Jj8YFmm3AgFC9O9LvF4v\nwWCQO3fulEV/bhokiaPNjQz2fQXPfR2LaSAhceL4MW7dusXVjz7m4vlyOdDo2BizsxlaDh/e5SsR\nLGWrbSi3klQsztTHH2MqCp7eXuYehnvdHrh4AeP+fdIeL/bpaVIjIwx9/jmdly6BCZPv/pJeh4Pc\n9DRDNjvHzpwGoOnRR7n9wa+ZzuZofPRREolE9Xy1nnAsnCBlMxnMmzfRTZg5/QiKrGx4orR04mXN\nZMh/eZOUx0M+Hidit2O63STi8fL2iRS3bztwu9u5dWuS1tYUoR14ejYzNcUhmx1ZlggUdHK5HA2+\nhm0/r0CwE5imiZZOlVfyY2WhbxQKNdJuJRSfH2uosbKSH0KyirRbgWApQvTvQxyotDSG+PLza7zw\n1ONAuSa/pbmJe/fuIWFW/YL9fh8vfeN5/rcf/O/8nz/6bzRFInxx8yte/863d/MSBDvI7Mws+VyW\nxqZDNUtS4hOTpMfHOdzTjcu9vL516sOr9AAUdT767DMOLXHP8Tc2ca+xEf+XX3D5zGnSAwOkOzvx\ner0oug6AXZbRCvP7BRojGF//Oq4f/zWZ//E/GLv1FRZFwXfqEfjeP6kp3BeVF3k8aIDpWb2RuF4W\nTrwUYNICqUwGyeEgmsngz+XR2o4CoBsGUP5dkywyRuU616KW81A9HOrs5O6dfnzZLOlgiEMNQvAL\n9i+mYaClEpW027JHfu20WxlrIIgy13gbCGFRhJwRCNZC/JbsJUwTtGI5BMvmKgdcsdxZR5Ikzp95\nhP/jh/+VUiFfbVKMJ5IcaW1F0zQsFgvp9DR+v49DTU38r3/wP/Per68yNHyfp596kksXzu345Ql2\nnomhYbQPr+K2SNw+1ETvlSsrbpeYmiL/7i855nDSP3SPE2+8scztxqw0pZqUS8w8X39qmXvOkWiM\n9BdfkMlk0Gx2ZElGlmUcZ8/R33cbzemkcwU3G5fNSrr/Dl+32tAMg48GBtiN51CGYZLNZOZvjCZo\nxzvg6Wfwh0MwPYPDZqX9cAsAkUiIrs67TE5O09EJkca1MwHWch6qB7vdTvdrr1IsFGlxuQ6Ug5Tg\n4GPqOmoyUXXWKcWjmFqttFsFJRiar8n3B0UQlkCwAYTo301Mo5xwOyf01UI1CMt0eME+v9q61F3n\nkZO9HG5u5r/933/BKy+9iMvp4subX/Hi88+hKAr/+f/6MzRV5Xf/yf+Ey+WitaWF3/4H393pKxSs\nk82uAC8lMzpKV2XVeq4kZSWmJyc56nAgyxINBY1CoYB7yWp/y1NPcfvjj0GROf61J5c57ZSKJWJv\nvU2rz8fI8DDWF17gSEM5MO5Ibw/09tQcq2mR0DExAWMNRyRd04kODJKTJPSGBhzFEiVMSpkMiqxs\n+DNLp1KYN29iWVBipWQyWCzlPohgIEgylVxU93/hwvF1NVpvVU+FLMu43K5NH0cg2G5MTaMUj1bL\nddR4DNOolXZrrYZgVdNuRRCWQLBphOjfSQxjscDXiqum3UpaEey1/6D/49/6TX7xzi/5z//1z5ie\nmeH82bOcO/MIAN/77X+EU7gT7Cs2swJsGiZT0SkymQwN3gZkRSYYCOI5coTRsQe4LRa0Q6sHiR3u\n7qZ/8C4NhSIz4QgtK9jXNQQDNLz0zVWPUSwW8Rs6h44ewX/oEFPB+oWtLMscfvRRvvjyC5Bkjp+v\n/SRq9PYtAuk0bqeDiXQao6uLbKmI/ZkreELBDSfpAugmLGwpVg2T3PQ0iUSCZCJJ7s2f47Hb8bvc\nZIrFtfsHTBj+6ia6ptF++vSW5AVs1EpUINgJDLWEGo9VnXW0RBzTrJF2a7NXGm4jWEPhchCWEPkC\nwZYjRP92YujllfyqyC8BK4v85Wm3azchdRw/RsvhZkZGHxAJhwkE/NX3hODff2xmBXh8Ypz/5/uf\n0ChbmbbZcR0/zhtvQPOxdmaCAQrZHL3Nzavu73A46H3jDQqFAq0bdHXyer08iDRSjMfJOBx0dXTU\nve9svoC3wUvb175W/boWDd4GnGfPYtqsZHN5wq+8QkNlorMZ4esPBJg5/Ui1NwCgFIvjvHoVy+AA\n1kyG4PAwWCQsF87jdTrW7B8Y+PBDWsbHUSwW+pMpep97dsPjm6PeFGSBYCcwisV5j/xYfWm31lBj\nZSU/jOxp2Jd5JwLBfkOI/q3E0OcFflXks7rIl6R5gV9pTlpv2q3D4eBE19p1xIKDTfLBGK1WJ0Fv\nAFchz/SCX+0Gn48Gn2/NY1gkC87N2LhaoOOpp7h/6xZNzYew2etzz9jIE47j584ymM1CJkPrE5dp\nbGrc+LgXIEmWsvvPktc8dvuiTAKN+puFjZkZXLaKV/js7Bpb189Sp6e94vIkOPjo+fwij3xtumyZ\nu5pHvuRyV1fxrUGRdisQ7BZC9G8GXVss8nUVWC7yq7c2SZ4X+Iqt/LVAAGCCbugbLv3wHWoibcTx\nqCozsozdvgtPekwY/NnP6DQMonf6iH7tKRqPtK6520aecMiyTPfTX9/oSFdl6QTEMExKw8OYn39O\nLJEkm8thZDIoioJaZ/+Av6eHgQ8/RDJNnOfPb/mYBYLtRs9mKs465cZbbXYGqJV26y2X64QiKKEI\nslP0nggEewEh+uvFNMFYKPKLa4t8WZkX+HMr+QLBElLRGJPv/hJF13GcPsORUyfXfQy3x4Olo4MH\nJQ2XL4Sm5mAD7aymYXLv888xikWOXrywrFm3FsVSEU8hj83tplVRGJiYqEv07yWWTkASiQS2jz4k\nfu0axWwOHci4nEgnT+Gvs3+gqe0o4dYWTMMUAVqCPY9pmuiZ2XKZTnSSUnQKPVcW96uK/AY/1moQ\nVgTJIYKwBIK9iPgLtBqmWRb1cwJfLYChrV6qAxWRb5uvyxeNSII6iH7xBb12B1ig/84d2IDoDwaC\n/NY/AFVVURQFi8W6oWbWwY8+4sjkBFaLxJ233ubkt16ue1+73U420shELEZakmjt7q6+N5NOM3W7\nD+/hZg61ta17XLtJayBI9FAzTXL59/mOpuF/4w06Ozvr7h+QZXnOzn/LWNj3sFmXJ8HDi2ma6DPT\n1VIdNbZG2q3FguILzLvrBMNItvoXBwQCwe4hRP8ci0R+5T9DX0PkWxc03pZTAQWC9TA7PcP4jRvo\nsRjO1la0juMbOo5FshD98ibOyUmyTiddL720oYZWPZPBUQm5kUvFde/f+/xzzM5miDgc1VVtTdUY\nf/MX9DgdjN8fJm63E67hJASACaVSqe6+gO3EIlnwBIO4shlU3cAXDhMM7a5TztIypPX6/AseXkzD\nQJtOlUOwKiU7NYOwLBKKP1BJuw2jBMJIVuvOD1wgEGyah1f0m2a50XahyDeNGs46lG+A1ZV8IfIF\nm2f8+nWe7epi0umkP5vhieeer7n9SlaNAFbFin9ykma3i6KqMT7QT/sjj6x7PIcunOf2L99F0nW8\nFzZWf+71ltecU9EYybEx3IcO4TMMAEI2OxPxeE3RXyqWGPjZT/EWS2QjkS1xu9ksrT09xCYnkWWZ\nJpdz15tmt8rrX3DwMQ0DNZlAjU1W0m6nMNTVg7DKabehssAPirRbgeAg8fD8Ji9Mu50r2akp8i2L\nV/IrjzUFgq3E2tBAJhajqbWVpGmuubK91KoRyqUd9m+9zLQk0QwkSkU84fCGxuMPhfD/5m9saN+F\npBMJpt/6e9qdLvoHBlDDIQqJJDmHg66uEzX3HR/op8swsbmcTESjzM5mqhOJ3WCujEapjEGU0gj2\nMqauoSbi8xaa8SimptUIwlJQguH5tFtfQKTdCgQHlIMr+ufSbusIwpoX+Qvq8WVFiHzBtnP83DmG\nJImJbJZj55YHUmmaxsAv30XO5XD19uIMBpZZNQKYVist33yRgVu3aThzdu3ymTqYvD/C7NQULadO\n4nIvD+uqxWw8TpPVhixLhHQN26VLeDwelDpWDL2RCNGbN2lVFNKSRGQXmwI3E5gmEOwEhqqiJmLV\nUh01EcM0agRhWW3VplslGEHx+UUQlkDwkHBwRL9hgLZgFX9NkS8trseXhMgX7AIWOHb2zKpv3//i\nSzpmZrBZFQY++wzblSur/tJ6fT68l5/YkmFNjYzC1at0ulzcHh2l94031lXD3tR+jMFbt/Bls6QD\nQU75/AuaYWoTamoi+rWn6B8bo7W3d1cdb0QZjWCvYZSKlGLR+XKdOoKwlAXOOnKDT3jkCwQPKftX\n9FeDsIr1pd1Wg7DmnHXWH4QlEOw0FllGr/zbqPy8Lk2r3Wi5ydT9EdK3byE1+Oh84olFon4mOkWn\ny4XFAl61RLFUxLGOFXeb3Ubva6+Tz+dpcbnAArlslvsfXAULtF9+Eqd7de/uxiOtO2b3mYrGmPrs\nU0yrjY6nntoTzcMCwRxGIU8pFq165Kvpck/PqiLf4aw23VpDjSIISyAQVNmfoj81tmra7eIgrIXO\nOkLkCzbHSk20c2UeKzXXBgObd3g5dvo0g9ksxuwsgXPniUQiJOsoNzENs+a5dV1n+sMP6XY5yU9O\nMnzzJsfOnK6+33ryFLfvj+BRS2QPNXNkAyU2FsmCa4Gwv/+rX9NbLDsC3f71r+l98RvrPuZ2MPnr\nX9GrKOiFIvc++YSup76220MSPMTouex8PX50Em1mGqiVduupCnxrMIzkcguRLxAIVmRfin6zMH/z\nE2m3gp0imUoy8z/+Pyxm2YkmUyiQ/OZL+AMBcn/zN8uaa3nt1U2XhlgkC11PXl70Wq1jziRTjL3z\nDoqqYnvkFG2rOPiYpolklqcOMmDo+qL3nS4nvW+8QUktrSuga8VzVSZL0+k0SaN8nuliYc2JyU6x\ncASmudu+PIKHCdM00bOZqsAvxabQM7NAjSAsb8N8020wguzchfRtgUCwL9mXot8CIghLsCtYTAPn\nnX5cdhtysUjyzZ/Di98ktFJz7Tacf7WnDXPieeKLG3QAE319jF6/Tum11+haoc5fURQcFy/S33cb\nPRDgxIJV/jkskqUuwb+ajejcuOYch1oNnYnh+wD4mptJppJ7ol4+/MRl7nz2GYbDScdjj+32cAQH\nGNM00Wdn5tNuY1Pouezq9plU0m4rIVjWUATJLtJuBQLBxtiXoh9vRIh8wa7hstvwOhxoQMlux9jB\ncy+17Fz6REHxNnD/2qccA/JuF7aREUoXLqxYp97a1QldnVs+ppXGNec41NrUBMDo5CSD779P8nAL\nnZcu7eqKf7j5EOFvf2vXzi84uJimiZZOlVfyY1OUYpMYhcLaabeVplslGEayiR4TgUCwNexP0S8E\nv2CXyBQKyMUiGpApqbsyhqWWnQufKHRcuMDH0SmmP79Oy+HDDOeyuGZmkCqieq0+g1KxxPhAP55w\neF22nyvaiNbYPn33Ll0eD84HDxiyWjl+frldqUCw3zANAy2dLJfqRKdQ49HaabeShDUQKodghUQQ\nlkAg2F7E3UUgqJNgIEjymy9ReveXmB4PbsBlmCTZOkedTWOBx15+mS+DIR785CccbW9H/vtfVMdU\ns8/AhIG/+zs6dZ34zZtMXr7MobY2AFLxOLFbt3E2RjjS07O5MZoga+XafpskoRcWf3bDN26gjo5i\nBoKcePLJuq0+BYKdxtR1zHQSM5UgdX+gEoS1etqtRZZRAuFq463iD4ogLIFAsGMI0S8Q1IlFshAM\nBcnI8782mWIRfyCAtIMBTgsnGCtNLkZv3SY/PIxsYVHJjalrGMbq6++apuHK57G7XbRYFQbGJzjU\n1oamaUy9/Q49TgcTI/f54PZtjpw8xZHu+WTdtSY9S98vRiIMqhoen42OC+err2ezWeRbt2j3eJge\nG+PmZ5/R3N62aN+tcEUSCDaCqWmoiVi16VaNx9DTaSjmybuWWNDKViyKFWswjFKpyRdptwKBYDcR\nol8gWAdLE1rnxP1OidDVzj9HNpuFG9cJZTIUvrpFNp3GUym7KWUyqM9cIRIJr3hsxapQONTExOQU\n05JEy8leoDwZcFdcd7IDAxwKh/HqOiOaxtFTJ9dMrV3p/aOs/LlZsGBW7AaTmQzGO29jaWmpvr9V\nrkgCQT0Yagk1Hpv3yE/EMc0labfFfPn/shWLzV5tuK0GYYlyVIFAsEcQol8gWAf1JLTe+/w6+v37\n6H4/3U8/vaUTgrXOv3Al3yFJuG12vBWP/XSxSGG1HSv0XrnC7GyGiMNRTcJ1OBwU2o8xcP8+D3SD\nr7W0YrMqRCuOPWuNaT2pti63C8v5CwwMDTHb1kb7DrkiCQQARrE475Efqy/t1nA1QIMf/8kzyN4G\n4ZEvEAj2LEL0CwRbSDabxdp3m+MeD5l4jAcDA4vKYFbj3ufX0RJxfF0naGo7uuHze70eEidPMvzJ\nNdw2GwGrlVSlZj5TUuv6hfd6l3cjdD3xODzxOFJHB2ODgxQMndbTy20+t4Ij3Seg+wSJRALefHNb\nziEQAOj5fEXglxtvtekUUCMIy+lebJ/p9jB9fxgApcG3k0MXCASCdSNEv0CwhUgWCa2y0lcyTRTH\n2j73D+7eJTg4gN/hYPDqB/iaGstlOguoVUKUy2YZ/sXfo5RK2Ht7aT97FvfhFsYLeVILfPZLxSLh\nQGDN8dTKAui4dBHt7FlkWa6OZ3Z6mrGPPwGrleOXL69oDyoQ7AX0XHa+Hj86hTZbO+1WdntRFqTd\nyi73Tg9ZIBAItgwh+gWCLcTpcmJ/9FEGhoaQW49wvK1tzX20QhG7RSIWjxMbHGSspNKcyxH2NQBr\n17GPXb/BSUkCp4OBW7fgkUcIh0JI//gfL9rOT33NxWtmAVgX3zYevPsevRbQs1kGP/yQ7meeXvMc\n9bJnXJF2gMnhYaavX8eQZdqeew6XWwjMzWCaJnpmdnEQVkXcr5526yuv5FdW8yWHSLsVCAQHByH6\nBYIt5nBHB3R01L39kZ5u+qemiN24wYnDLVhlmclolGDL4eo2terYrQ0NZCYm8NhsqFYrWMBiqb+O\nfiVqZQEsxWIYIEvIkoSpaRs+51LWahA+aKQ/+5weqwIm9H3yCT1Xruz2kPYVpmmiz0zPr+THptDz\nudpBWJW02zl3Hcm29pM5gUAg2K8I0S8Q7DKyLNP73LMwneaQojCZTK0roKf9kUcYMgzGZ2Zo22TI\nlWmYJBNJrJlM9eZg1rD5BAg//jh91z7BVBTannh8Q+fNZ3Pc//hjLLJMx+XLKFZlXQ3ABwGzYuVY\n0jRkIT7XxDQMtOlUOQSrUpNfMwjLIqH4l6TdWq07P3CBQCDYJYToFwgq9P/6AyzRKfRgiJ6nn97x\nUKjW556j75NPmG2yElJLJDNl8bJmSYsFjp09syVjSKaS5N78OcHhYSx2O7liiXz3Cbw19gk3HyL8\nne9s6rzD775Lt6qSmJ3h05lpOi9fZuLeENZkEvuhJtrPnt3U8fcDrVeu0HftGrLDwbHHHt3t4ew5\nTMNATSbKjbfRSdT4FIa6ehAWkow1ECyv5AdF2q1AIBCIO6BAACTicQIj94l4PCQnJpgaH6NpgT88\nQLFYxG6zb9tkwOvz0fPCC9VG2rn1dSmXp8HbsD0nXQGP3Q4WCQ0oYZIpFGqK/q3Aoqqkc1kKN77A\n7XJSjCdQP/+Mw088gZ5KEQ1HaFxQ7nQQ8foa6Hn+ud0exp7B1DXURHzeQjMexdS0Gmm3CkowVG26\nFWm3AoFAsBgh+gUHjlruM6ths9lJV/5dwMS5oIHPNExuvfkmDek0M24X3S+/jLKNK4YLy1ru/OrX\nuB6MMijJtLz4DRr8/kXjWnqdsPmwML/LjeXCeUzKgV72Z65sqo6+nnGGL11i4J13cFutHD95CiwW\nipXSC5sskVVLGz6/YH9gqCpqIja/kp+IYxr66iLfasUajFRX8hWfXwRhCQQCQQ2E6BccONZyn1kJ\nb4OXmUcfZeD+CI4T3QRC8yI3kUhwKJUk5PEwm8sTffCAw+3t230Z6LqO9cEDjlRcXAZu3abhycvV\n95dep2mYPEglSb74TYKh+TTc9U4ALJJlcRNvaH3HKBVLpBMJAPLpNFafD/XnP6+OE5Z/T8Ith7G8\n/BIWWcLlKk+4CuEICV3H23qE7rb2dV2DYO9jlEqo8Wi18VZLJpan3c4xl3ZbddappN2KICyBQCCo\nGyH6BQeS9bjPzNHS2Qmdncted7vdPABCQNw0CO+Qe4wsy+SdTjRdJ1koYH59pwAAIABJREFU4j7Z\nuGybhdeZzGSw9Q9gtduxeDx1TXZWYqFNZq1+gpVW8N1uN/f+9ie4U0nGBwZ55MIFbpZKdNqs607W\nbWo7ivniiw9VM+9BxigWKEUrSbexSdR0qnbarcM5X48fCiN7RNqtQCAQbAYh+gW7QjoWI/PZZxTD\nYYKPP07jkdbdHtKqOF1OIi++yMDdewTbjuL17Vx9fddLLzHSdxt3IMjho0fW3N5js+LzeGioCOx6\nJjsLWWqTmX8wRv7Tz5hpjHDs3GJnoJWeqMQvX+awppEqFnnEqqAW8jToBlqdtdX1TjgEex89l52v\nx49Ooc2UC+hWTbt1VdJuKzX5ksstRL5AIBBsIUL0C3aFTF8fPTYbEbuNOzdu7GnRD+ALBNEr1vuJ\nSunKZmvn68Fmt3F8B51rFvYTFItFUre+otPjIdU/wLjPz+Fj7Yu2n3vSEH3wgGwsjkXXmFBkAt4G\nrj0Yo0tRmPE4OFRS1zz30gnHfvLkH7j6IYw9QG3w0f38c8gPYQOpns0sSLudRMvMAjWCsDwN1RAs\nJRRBdrp2esgCgUDwUCFEv2BXMOx2tFIRXTcwPFufernVK8Yb6RPYCRZeZyqTYSabQ89k0Fh+3cVi\nkdmZWQLBQF2i1NAN5tziHYrCTD6/4naxyUlc4xM0Gzr9H3/C6X/4D5lJJXn0N38DTdPo0jQyf/03\nLFy0Xel7sl99+WdnM7iGh2jxeMjPzDA+eJcj3Sd2e1jbimma6LMzi9Nuc9nV7TMBuRKEZQ2GsYYi\nSHbH8m0FAoFAsG0I0S/YFQ5fuMDgVzfJRCJ0XLq0pcferhXjjfQJ1GL87l0yAwNY/AG6Hn983Vag\nS6+zwTAxnrmCOxDAlCyLrjufzTH8k5/QZOj0OV30vPLtqvAvFArouo670jA8h9PlRO3pZeD+fTSf\njxOrCFmtUMApy6iGjt3QURSFQ63zT25Mw8RygJN1bTYrc9OhjKZh97hrbr8fMU0TbTpdDcEqxaYw\nCvnaabe+JUFYNtvOD1wgEAgEVYToF+wKiqLQevYcx461b/mx98OKcalYovDJNU64Xcw+GGU0EFhz\nddg0TIZv3kQrFTl27hyKoiy7zkgkvGy/+MQktz/4gDO6hs/jQc9mmZ2dxe/3M3n/PvmrH2LFZLKz\ni45LFxfte/z8OaiR8jubL+Bo8HFraopCNofc0YnNvljc7Yfvx2aw2+34vv51Bu7ew9HRyZEl+Q77\nEdMw0NLJBY23a6TdShKKPzhvoRkIibRbgUAg2GMI0S8Q7AKGaWCprH3LFglD11fdtlAoMH7nDsmx\ncU4VClglCwOpNL3feGHN88QnJym8+0vOmCY3btzg7LlzxGx2TlSeWMwODNLlLtdSDzwYhSWivxZz\nTxoADvPt+dceQiKtrURa93ZfSi1MXa+k3ZZLdUqxKKa2etqtRZZRAmGsoXB5JT8QEkFYAoFAsMcR\nol/wULKRYKut7BNwOBwoZ84ycO8uejhMd0/PquO899OfcUKyMHX9OsXjHVhcTqSVxNgKzExMctTh\nRJElWnp6iZ05S2dHRzVczBoJk7jTj1OW0YLrE+wHfQX/IFNNu42Wy3XURBRTrxGEpSgowfB82q0v\nIES+QCAQ7DOE6Bc8lCxtzIXazbnb0Sdw5GQvnOytuU2hUMBbKqG4XbS3tvLWBx/Q5vWS6e7GeuMG\nejyB5/hxmo+1r7h/c3c3/ffu0VAoUDpyhN4l52s/e5YJv59coUhPV9emrmevMT40zOS9ezTtQJDa\nXsdQS6jxWDnpNjaFmoxjGjWCsKy2Rc46SoNIuxUIBIL9jhD9gn3BRlbm12JpYy6s3py7W6vaTpeT\nTCTCWCzKsKry1OXLhH0+7k1OYn7yCV1NTQx99CG5xkZc7uWWh06Xk+7XX6NULNHqWtklqbmtbbsv\noybFYpHk5BSBpkYcjq1xdLn32ecEBgc5nEwyHI9zvOP4lhx3v2CUipRi0aqzjppK1A7CsjtQKk23\n1lAE2SuCsAQCgeCgIUS/YF+w0sr8TC5P4qmnaG1tXVHwHhR6n3uObC7LyWKJxN/9HWEgquv0eL0A\nOEwTTdNW3V+WZZyrCH4oNxXffe89pGKBwNlzO5qZUCqWuPc3f0urrjNssdD2yis1x1oveiJBwOlA\ns9mwzs5swUj3Nno+jxqfqjTeTqKlU8AKHvlQFvlOV1XgW0MRJLdHiHyBQCA44AjRL9g3LF2ZH+vr\nw/G+wZTHg/eZK4SbD+3i6LYRC7jdbtxuN5ZvvsjwyCinrzzD2PXrJFMpOHac5k2kBN+/do0T2Syy\nLNH30Ucrin5d17n3yTWMYpEjly7icm+NLeX09DRNagmvx0NrvkAqOoVzC8pxPMePM/TJJ6RzOcwD\nuMqv57JVga9Gp9Bmp4EaQVhuL0ql6dYaiiC7Dp6tqEAgEAhqI0S/YF9immDN5Qm53QTdbgbu3l23\n6F/YmDv3tcswq4m7C9mq9N2VypTWc2x/KIS/UmbUcOXKpscDgCRhVgqbzFVWe+99/AlHJyawKwq3\n33qbrpdf5u4HH2CqJVoefQzvBicddruNT+7epblYInvsGGeamzd8GQtp7jhOoeUw+bv38Lv291Mg\n0zTRM7OosbJ9Zik6iV4R96uKfK+vuopvDYaRHFsfgCcQCASC/YUQ/YJ9icUCqt3OTC5PFvCdO7+u\n/Zc25kK5OdcwzHU1+C5lLVG/Vcm+xWKRoQ+uYuoaR594ArentpeQruv0v/c+yvQ01mPttJ89W33v\n2KOXGPzVr6FQoPGxx1bc3ygUsFvLtwtJ1bh79SodqRSKLHH7vXfp/c531jX+OcavfcrTp04xnc0x\n7nBgt9vX3qlOHA4Hzn0o+E3TRJ+Zrlhnllfy9XyudhBWJe1WCUawhsJItq37HAUCgUBwMBCiX7Bv\nWLoy725vI3fpEq3tbTT4fOs61mqNuYlEYl0NvkupR9RvRbLv0K9+TXc2iwW4/e579H77WzW3f3Cn\nn/ZUCqdVYeT2bfJdJ6q184qi0HPlmZr7H75wgdvvvINF0/CdP8/08DBKxc3FohsbuIIKsoQiK4R9\nDcQ2G3G8TzENA206VbbOrKzk1wzCskgo/oVptyEkq0i7FQgEAkFthOgX7AtWWpn3Ace2qOxmK9kK\nUb8Wpq4zV4ljMdcW3YrdTkFVyRcLxLM5rOk0uXyu7tIir6+B3tdfq37tO9TE7Xd+iUXXCT16acPX\ncezyZfrefx80nZZVnjIcNEzDQEslqqU6aiyKoZZqpN3KWAMhrKFweSU/EMKiiFu3QCAQCNaH+Msh\n2BestDK/HTaeO8FWhHy1Pv44t99/H4tp0PTY42tu39JxnM9GRtB/8QtChw9jf/eXGy4tAnC53fS+\n8u0NjHwxdrud3hfWThbez5TTbuNVj/xSPIqpaTXSbhWUYKjadKv4gyIISyAQCASbRoh+wb5lvQFb\n9bJSg+96hHktUb9VIV9eX8Mi0T0+PAwmHG5vh1XmO21nTmOZnNj2pxAPO4aqoiZi1VIdNRHHNGqk\n3Vqt8wI/GEHxiSAsgUAgEGw9QvQL9jWbqb9fidUafNcS5ulEgon3fwWmgfz443gONa2473aEfA18\n+BGRkREsFhiYnKTr8hNbenxBbYxSCTUerTTeTqEl4phmjbRbm32Bs04lCEuIfIFAIBBsM0L0H1Cy\n2SySRdqSoKOHiY2K8olrn9IrS4BE39A9QqdOrr6xCRMjI8iKQmPL4Y0Pdo5kEn/laUc0ubzcaSFb\nUVr0sGMUi5Rik5XG20nUdKp22q3DucBZJ4Ls8YogLIFAIBDsOEL0H0CGb9xAvnULw2JBOn+BI90n\ndntI28ZmS3G2Covdhp7PI0kSpq22k8rABx8QGRtDNQyGero5du7cps7taG9n9MZ1AGynT6+63VaV\nFj1s6PkcaixKKTpZXsmfXiXttuKRL7k8WENhrKHGske+yy1EvkAgEAh2HSH6DyDq6CjtlZKXgaF7\ncEBF/0ZLcbaD45efZPCjjzANg2NPrNFYm0xUV+bT0Vj15Xwuj9VqRbGu79fyyMlecm1tmJi4ayTl\nbkdp0UFEz2aqAl+NTaHNzgA1grA83vma/FAE2bn/sgEEAoFAcPARov8gEggyOzGOZphIzVtQPrJH\nsUgWknf6IRqFxka6nry8a2Ox2W10P/31uraVW1qZuNOHBjhOdANw99qn2Ab6KVgkAs88s+50YZdb\nCM2NMJd2W4pNoUbLJTt6bo2020oQljUYxhqMIDkcSw8rEAgEAsGeQ4j+A0jXk08yencQxWqlo61t\nt4ezbcSmogRG7hN2u4ndHybe2Um4MbLbw1qT4xfOM9PRgUWS8HrLT2SM4SGOzD2dudNHqKlpX9qR\n7nXKabdpStGp6mq+UcjXTrv1LQzCCiOtUb4lEAgEAsFeRIj+g4gFjnR27vYoth2rzUbWLBf3lAC3\n1bq7A1oHDb6GRV+r3gYKmQw5Q8d27Ni22ZE+bJimAbMzZPu+qpbr1Ey7lSQUf3C+XCcQQtpHP1cC\ngUAgePgwDIPUWBS1WAS/b9XthOgX7Fv8AT+z584zMPYAR2cX/oB/x869UjDYZlbhTzz/HKO3+3B4\nPbS1t5NIJNZlR5rNZBj55btYTIPIo48SOrS+8qCDgmkYqMl4xSN/Cv3uAGga0/IK3xfZikWWUQLh\ncuNtsCzyRRCWQCAQCPYyuqaTGpsiem+U2PAYifvjqMUSNtnCif/ld1bdT4h+wb7mSG8P9Pbs+HmX\nrsRvdhVeURSOnX5kw+MZ/fBDeg0DLND3ySeEvvOdDR9rP2HqGmoiPt94G49i6guCsHK58v9drrLI\nVxSUSi2+NRRB8QWEyBcIBALBnkYrqSRGJogNPyiL/JEJdFWjND2zaLvSGscRol+waVZa9Ybtrz8v\nFAqMfn4dxeXi2JkzqybRbhdLV+K3Ot12XXakioJuGMiShHmAg54MVa0GYamxKdREDNNYPQjLUGzQ\n4Mfd1YMSiqA0iLRbgUAgEOxt1EKJ+Mg4sXsPiA0/IPlgCkPXl4l8AIvTjd3pINTWQuhobfMWIfoF\nm2a36s/v/eIX9JqQV1XuahodFy9s27l2mvXakR6//CQDVz/A1DSOPrFyIq9pmAzduIGWyXDkwgWc\n+8DxxygVKcWiqHNhWKlE7SAsuwOl0nRrDUWYTSTAYsH5/7d350Fu1meewL96dd93H+rb2G3j28YG\nm5gEGGBIMhjI5JjJujZeNknNLgM1xQSSPzKTVDIzVCUDUyGkTIUNM0ntshWSTUJCoAZjCDcxOD4w\nGJ9Sd6sv3WpJrfN93/3jVasvq9t9t7q/nypXuXW8egWvX337p+d9nvaOxd95IiKiK5AfziIS6FNW\n8v1BxPuUBa1qId9gMcHTqoR8T1sTLB5nZR5Ml5iv+joM/TQvZlJ/Pl/U+TxUej1MOi1KQ5P/YcyU\nLMmQZAnqZVDuMdOe+jq9DhtuvHHKx1z605/g6+6GUaPBmZdewtV37p/jXs4/KZdFoVyPXwwPophQ\nvkGqGvKNpkpnHa3bC8FsGT8Ia5oJxURERIstl8ogHOhF2B9EyB9EciACAJNCvsqozN4xO2xwt/oq\nf8xO+6yGPjL0U80ybdyE8++/j5JGg6Zt2+a0rejgIMKvvgaNJEK/dRtaNl497XPGlt8s1STgmSil\n0zCWB38Jhekq/xaHOJwZ7ZEfHkRpKAlgih75Zis07tGafLWp+jAyIiKi5SCTGELY34tIQAn5qbAy\n2b1ayLe4HJVVfHerDya7dV72g6Gf5sWM6s/nScvGqyFv2DAv1w1ETr2PDeXypHPnzgLThP6J5TdL\nNQl4Jnw7duDMy0cglEowbZ3bL0mzIcsyxEy63FlHCfliOgVgipBvtY1edOvyQm00LvZuExERXTFZ\nlpGOJior+ZFAL9IxZUGrWsi3ed3jQr7BsjDltwz9NGczrT+fT/N1obDa6cBw1xB0ajVE4/T/2GZa\nfrMcWO02XH333Yv2erIsQ0wllUFY4QEUQ4MQs8PVe+QD0NidSnedcsmOoNcv2v4SERHNlCzLGApF\nEfb3lmvye5EdSletxxdUKtgavPCMKdfRGRdnsjtDP81ZLQbgidZeswsBgwHFXA7rtm2ft+2WiiUE\nz34Es8sNr69xXrYZHRhA6K23oQLgvu5aeJua5mW7cyXLMkqJuLKSH1aCvpTLTTHtVoDG4Ryzku+G\noOW0WyIiWr4kSUJyIKKs5Je76+Qz2eohXxDg9NVVAr6rxQetfmk+6xj6iQBABbRvnn2f/GrOvvAC\n1pZKiBU+QP9116Gxo33O2wyfOIGry7X5Z4+fWLLQL0sSSvHo6IW3kdA0027V0Dpd0JRDvtbphkrD\nUxARES1fkigi1jtYrsnvRTjQi2IuXzXkqzVquJoaKiHf2dwAzTKZ7M5PXKIFIooijMPD0JtNaNRq\ncL6vD+hon/uGDQaUcnmoAEiWxatxl0URxVgUhdCAspofCUEuFauGfJVaA43LPbqS73BxEBYRES1r\nYrGEWHAA4UAvQpeCiHb3oVQoVg35Wp0WruZGJeS3NcHRWAe1Znl+1jH0Ey0QtVqNgs+H3r5epAQ1\nGjasn5ftrv3YPvjffReQZVy1a9e8bPNy5FIJhUioUq5TjIQhS2L1kK/RQuvyKH3yR6bdchAWEREt\nY6V8AZGefkTKNfnRnoHLTrsFlJCvM+jHtc+0N3gh1MhnHUM/0QLa8PEbkEqlUW8wQKOdn39uGq0G\n667fOy/bGksqFlCMhCuddUrRCGS5+rRblU4/pke+B2qrnSGfiIiWtUI2h0hXX6UmP94XmnrarclY\n6arjbvXBVueeVY/85YChnxbdhx/04O23e2GxFNHa2jJvw7BkSUYsPnkYk8vpmrcuP7NhtS5s81JR\nFHHhrbeBVAr2jVejob39ip4n5fPKCv7ItNtEbNppt1p3XXkl3wO1xVazJz4iIlodcunhSi1+ONCL\nRF8IsixXDflGq6XcPtMHd2sTLG7HivmsY+inRVXIF/D++yUIQieSyTw+ON2FrdvWzMu2Y/EY0s/+\nFtYxra9S2Rxw5/6a7y40la5T76M9EoZeo8G5o+/C23L5X6TEbHZcj/xSUhkOUq1HvmAyV1bxte46\nCCbzijnxERHRyjScTCMSCJb75PciOTjNtFunHe6WkZDvg8mxche0GPppUQmCAJVKBABIcgk6/fwe\nglajAS7L+JV1ucpjVwoZgKr8JlWSVLldzKTLrTOVibellHLCqzoIy2IdvejW7YX6CuYVEBERLRVZ\nljFcnnYbKg/CSkWmnnZr87pGa/JbfDDaFnqU6PLB0E+LSqPVYM8eK1597Sw8HmDD+n1LvUs1r33r\nFpxPJKCKRWFqqkf63beUabflcF815Nsc0I4dhGVYnOEgREREsyHLMtKRuNJZx68MwhpODFUt1VEB\nsNV7xg3C0ptX74IWQz8tuta2euzbl1V+WIRv0CRJRjQaHXfbUtf5z5UsyxCHEuVpt4NwD4UgFoch\nnetHZuKD1VpApYLG7hztruPyQNBx2i0RES1fsiwjORhRpt36lZKdXCoz5bRbe2OdEvLbmuBqbli0\nabe1gKGfJrncBbELHZJFUcTQUApWi2VOXW5S2dyknwvxOHRvvFGp9a/FOn9ZklBKxpULbssX3k45\nCEslQON0VWryNU4PhGUyHISIiOhyJElCoi+McEBZxQ8HgigM56oPwlKr4Wyqh7tV6a7jam6ARsfP\numoY+mmSiRfELnRIFkURH/3+edRl0jiv06Pj05+CYRalJi6nC7hz/7gafguUlX71hFr/5V7nL0sS\nirHoaGedyCCkYvVBWMq0W3elHl/rcHHaLRERLWtiSUS8d7Cyih8J9KKYL1QN+RqNBq6WxtFpt756\nqOepHfZqwP9SdFkTL4hdyJA8NJRCXSYNr8UCS76AcFc3WtZ3XtFzr+RbiYmlPcuRLJZQjEaUC29D\nAyhGQpDFqQZhaaBxeUYvvLU7Oe2WiIiWNbFYQqS7X+mu4+9FtKd/6mm3eh3clZDfBEejFwI/62aN\noZ+WlCzJyOdz8OdyKOTzGCiVsK6h/oqfv9jfSkyUy+UgSzKMJuOMnicViyhGw5UWmsVoZOppt1pd\n+YJbpSZfY3NwEBYRES1rxVwBke4+hMuddaI9A1MPwjIa4Bppn9nWBFudu2am3dYChn5aUrF4DPnn\nX0CHIGAokYBZp0OxVJrRNq7kW4mxtf6pbA7z0aCr7+JF5N99F2oA8qbNaNuyuepjpUIexUhIufA2\nNIBSPDbltFtBb1BW8t3KSr7aal+xfYOJiGhlKGRzlVr8sL8X8b4QZEmqGvINFpMyCKtck2/1uvhZ\nt4AY+leIQr6A6MAAHF7vjFedL2chQnI1I6G9we1CLJ2e91KiibX+lpHb5ihz/gLWmZW+v+e7AsCY\n0C/lsiiEQ5WJt8VyCVLVkG8wQuuuq4R8wWzhiY+IiJa1XCqDcFdvpbtOciBy2Wm3Iz3yTXYrPG1N\nlZp8s5MLWouJoX8FKBVLuPDcc2gpldAly2i94w6Y5tCHdqFC8lJRCapJ5T7yZdp4AjPrUqRyu5EM\n+KFWqSA5HMh2XSqX6wyiNJQAMEWPfLMFmnI9vtbthWA08cRHRETL2nAyVQ74PQj7ezEUVha0qoV8\ni8uhrOS3NcHd0giTw7bo+0yjGPpXgORQEvWFPKwWC9ryBUT6+2Bau3bW27tcSF5KmYzSed5cXlWf\naDbfSky8FqCynSu4HkCWZYiZNJo8Dgz0C5DiUdiLKSQjweoh32obvejW5YXaaKxsKxaLARNajbpc\n/IqTiIiWjizLyMSSyiCsSz2IBHqRjiUBTDXt1j0a8lt9MFhW7yCs5YihfwWw2+04ZzBAyAxjQBDQ\n0dy84K8pSzJEUZxTT/0RU4X2rtOnIbz/PgAgtm07WjZePe65c/lWYuK1AMDlrweQZRliKlnuka/U\n5IvZYUiZNEbGW0mlgvKXcsjX2J2jNfkuDwT95VuQxmIxpH97mV8+9tfWHAEiIqptsixjKBxDZExN\n/nAyNeUgLFu9Z1y5DgdhLW8M/SuARqNB51/cgXgshqscDuj0ugV9vVRyCMHDh2EolVDq6MC6666d\n9bamC+2F7h6sKwfz84EAMCH0L8S3ErIso5SIKwE/PIhCeABSLjfFICxVJeQPa/QQ7E6IWi1EAHkA\nrmkm317pLx9ERETzRZIkJAciCAd6K911cunh6iFfEOD01VUCvqvFB+0C5w2aXwz9K4RGq4G3vm5R\nXmvg9GlcrdcBeh0u+P2Qd++e9bTeaUO704lksEcJwS2ts3qNaka+YZBlGVI+h1QyAd3RNxHOZqae\ndisI0DrcyhAstxdapxsqjQbRaBRZrtoTEdEyJIki4n1hRAJBhPxBRAJ9KGSnmHarUcPV1DA6CKu5\nARpOdq9pDP3LVNfp0yhc8qNks6LzhhugXkbDKAweD2J9vXDqDcgb9LMO/Fdi3Z496OtqUP7e3j4v\n25RFERaxhMLGTgxHwxDjUUBVhKAHVH3dGNcwVK2FSq2GxumB1u2B1l0HjcNVdRDWbFbtUxPq+Re6\nWxIREa18YklErKd/dCW/ux+lKabdanVauJpHp906fPVQa5ZP9qC5Y+hfhvL5PKTTp7HObEY2FkfP\nmTNo31y9B/xia1nfiR6VCtFEAldt3bKwL6YCfHMM+3KphEIkVCnXKUbCkCURciYNAUBl7IdOV552\nq60MwdK6PAs67dblcgH794/7xcAycjsREdEVKuULiPYMIOwPIhwIItozALFYqhrydQZ9JeC7W32w\nN3g5CGuFY+hfhlRQQSz/vSSJUGtHa+ZkSUas3PN9xEzaTM6Xls51i/p6MyEVCyhGwiiEBlAID6IU\njUw5CEul00Pr8ih/PHXKIKxZnvhmumqvUi2vTklERFQbCrk8ol19CPt7EfL3IN4bmnrarclYCfie\ntiZYvS6G/FWGoX8Z0ul1MO/Zg3PnzkNoaMTazs7KfRNbTV5pm8mVTMrny0Owyiv58Sggy1NPux2p\nx3d5obba5qU95mKu2gdOnkTh/DmU9AZcddtt0OunvliYiIhqW2E4h+AHF8or+b1I9IennHZrtFpG\nQ36rDxaPk62gVzmG/mWqsb0djVXKWibWja+2Ti9iNlsO+APKIKxkHED1QViC0VweguWB1rVw024X\na9VeFEWUzpxBp8UMUZRw8fhxdO7Zs+CvS0REiyc7lK7U4587fhqZaAJWi7Vqj3yz0w53iw+eNiXo\nmxzzs6BFKwdDPy174nCmUqpTDA2ilFKGg1SfdmtV2mdqlfaZstGEAgDLChl4JagFFDTKP93hYhF6\nq3WJ94iIiOYqE08q024DSp/8VFhZ0Cokh5AZHlb+LsqVkG/1OMdMu/XBaGMLCJoaQz/NSjQaw0W/\nH16PBx3tbQCU1pdzDdWyLENMp8oBXwn6YjncV592ay+v5CvlOoLBgGg0itxvJ5RBrZDWmSqo0HzL\nLTh76hR0LXa0b9q41LtERLSiZYaHEYnF4auvg1arnfPnnSzLSEcTSqmOvxchfxDDiaGqpTooSTC5\nnVi7bWOlZEdv5rRbmhmG/ho01QTbxfD6m2/jN7/7PWw2KwRBwLYtm7H/05+c1bZkWYY4lBxdyQ8P\nVqbdTjIyCMvmgNbthcallOwIVYZfreQyKJvDAdvHP77Uu0FEtKKl0mn84nfP4dSHZ+ByOrGmtRW3\n3fgJ1HlmtoAkyzKGQlFlJb/cXSc7lJly2q29sQ6eVh9cLY1IyyVo9Dp0dLTPy/ui1Ymhv8ZMN8F2\nocVicTz73PM48Nefx7Ytm3Hs+An8x/9+Ghs616Fz3dor2oZcKkHqvgQ5HkX4+NtTD8JSCdA4nJVV\nfI3LA4HDQYiIaAGNrOSfOP0BBkJhfOtrDyAai+O5l17CL5/7Pf7nwf96xdt655kX0H/Wj8LwFIOw\n1Go4ytNuPa1NcDY3jJt26/cH5uNt0SrH0F9jpp1gO0MjLUBj8TgczX5CAAAaUElEQVTePXYcoXAY\nm67egBtvuAFa3eRwfdHvR53Xg/bWVgiCgN3X7MR7x0/gzXf+iIb6OthstulfVBAgfXAcEEWUTGO+\nnlRrAUENrdMNrdujrOSXp90SERHNl1Akio/On4dao8H2TRthNpnGleyoVCqUSiUcP/0Btm/eBKfd\nDqfdjr+68048/NgP0d3bh9Ym37Svkzh3EQl/D9L9ocptKqMZGo0GrpbRQVhOXz3UWn7W0cLiEbbK\nRWNRxH/1a/z63WPIl4pocbvxi/88jP6BQXzuM3fBaDRClmXIsgxBEBCORGE0GiBJUmUbO7dtxR9e\newN9/QNVQ3+hPzjuZ5XVBjGRhEpvLE+7LQ/CmmLa7UwtdRkUEREtP394620cef0N2G02aLUaHH71\nNdz33/8b3E5n5TGyLEOj0WAwHMF1O3cAACRJQkOdF76GBrx74gRafI2T6voT5y5Oej3v1RuQTAzD\nVe6s42rxwdFYx2m3tOgY+lexkVWN9y75kcnncd+f3wqPzQqvzYrefB7RWAzNTU1QqVSQZaWgqKG+\nDm+988fKzwBwVUcHXnn1dQyGw9iwXpkpMDHkAwB05spfxZa1QIsKrk1bZj0IayoTe+av5im3mXQa\nsYEB1LW0sJ8/Ea0aoijiqf/7c3zlwBcrtw2GI3jx1ddw9ydvx+7t25DOZHDoP36Gw6+9jr+45c9g\nMZvHfb65nA70DQ4CUEK/IAjYvGE9Tn14Bvl8AbnuyZ91Gu/4bwCusnux7mPXcBAWLTmG/lVk4jRf\nZQUfiKbTaPN64LEprR/3rlsL+bbb4Ha7K78YjKxmrLtqDWLxOBLJJDzlC5k8HjeE3DCiAT+G25qg\nGSnHGRPyJ7HaAWBBAj/ASbcjUqkUBn7/HJo0Wlw8dQqd+/eP/v8hIlrBPjh7Dqc+PIOuYC/ampsA\nAKc+/BAelwtr2loBABazGds3b8KpMx9hOJuDxax8bo185rU2NeFioKuyzcS5i2jW6vH8xUsY/PAj\n2C2WSSF/Is1lSmWJlgI//VeRidN809kcUrt3o1AqwaDV4lx/P14+fQbhoSHsdLpw8yc+DqPRCACV\n1X6r1Qqb1Yrzx4+h2aCp/EJgNZuRyhUBnRmyWo0zR45AG4+j5PViw42fgAq13x+/FiX6+tCs1cGo\n18GbTiOVTsHpcE7/RCKiGjaUSuHwa68DAM5dvFgJ/Ve1t6HO44HZOL6GfyiVgsflnNSKs06lxitn\nPhpXtuPr3ARJ+zyKZgc03rpFfFdEczOjZdYjR45g586dk24/dOgQbrzxRmzfvh333HMPLl26NO7+\nQqGAf/mXf8G+ffuwc+dO3H///QiFQpO2QwvPpNPCpNXCZbHArNehrr4emq1bUdy4EYclFbB1Czbc\n8Rd46ZVX8evf/R5DQ0qngXxfD/J9PSj0B7HBV4+LwUEkciJUegugM8Pu8iCeHIJGo0FfVxea4nGs\nMxrg6e9HLBJd4ne9enlaW+GXJETSaYRMZtisV3ChNRFRDcvl8vjNC/8JjUaNnVs34/jp0wCUb7c7\nWluxdePVMBj0UKlUKBaLePfESay/ag0EQUDy/CXl4tvyn85t10AwWnA+mYHG64PG64Msy7DbbEik\nJnfiIVrOrjj0/+lPf8KDDz446fbHH38cTzzxBL785S/j0UcfRSqVwsGDB5FOj7Zg/Na3voVnn30W\nX/va1/Dwww/j7Nmz+OpXvzruYlBaeMPpDAbefx/p48fR/cGHEAQBarWAq9aswZmz56DWanDwwBfx\nub+8G//lz/8MgTMf4ujLR1DoD0LWmpRyHZ0Z1+zcjeFcFu8c+xMAIJ3JoCvYC19DPQDAbLcjJYrK\nfQAM5W8LaPEZjUasvfNOqG69DRs+/Wmo5+kiaSKi5apQLECn1+EL+/fjup070Deg1OSPfDM9diX/\nxV//Fn3+LuzyNSNx7iLUnsZKuFd7GmG1WLB+zVV479RJROPKhNwPzp2FXqeD1cz2EFRbpi3vKRQK\n+OlPf4rHHnsMJpMJxWKxcl86ncZPfvIT3HfffThw4AAAYNeuXbjpppvwy1/+EgcPHkR3dzeeffZZ\nPPLII/jkJ5UBThs2bMDtt9+OI0eO4NZbb12gt0YTDV68iA6VALfBgGAigXcLRXRlf4dYbxA2jYAm\nsxFCPIICgI6rOlF39hIu9YXwcZ0ZKF/ABADr1nRgKJ3G07/+DaKxOOLJJADglhv2AQAcTieye67D\nuZ4gHNu3w2yeorZ/gQUvnMfwhQsQPB6s3bV7yfZjKel0Ol7fQEQr3shCos1qxV/duR8AIEPpPtcV\n7IV9OFe5SFelUiEST+APH3yEG264Eb71myFJ0mWn7N50/fV47shLePrZX2Pn5i04/sFpdK5Zg6aG\nhnmZRE+0WKZd6X/ttdfw5JNP4utf/zoOHDgw7qr2kydPIpvN4uabb67cZrPZsHv3brz+ulJL9847\n7wAAbrrppspj2trasHbt2spjaHHY6usRHB5Gb3oIP/nwQzz73nvIRyPYtWUTPN4GDMSHKqv5JqMR\nmewwHHblgltBENAV7EUqnVb682/fhnsPfgkajRpr2lpx4C8/U7kACgAa2zvQecMNqGtuWaq3i2w2\ni+J7x9BZKsEb6EKv379k+0JERAtLEITK4pQsy0icuwh5IAy7oMa7r7wKAFB7GqGta4La04iXPvgI\nWq0Gn7huT+X5YyXK5a1tTc344p13o6m+AUdPnsCa1lbcuGcvADDwU02ZdqV/y5YtePnll2GxWPDD\nH/5w3H2BQAAA0NraOu725uZmvPzyywAAv98Pr9cLg8Ew7jEtLS3wM4TNK1mSIJeKEHTj2zKOtM9s\nc9rQ/VefQzQWw2c/83k47HZ4PB6oVCpojMfw/557HsdPn8a2jRsR7OuHv7sHn9irnAwTySH8r//z\nNO7+1Cexc8tmAMDajnas7WhfxHc4M7IsVw5wNQBpzLdURES0cuTzeRw98gecvnARJoMet+7ZA5PB\nAEtTOzo6r8aFSAS3e30Qy6Wnl7q7cOrMh/jvX/hrWMxmSJKEUCSCQqmIVl8TXn7rTbz53rv4xv+4\nF1qtFi6HA5/55KeW+F0Szc20ob++vr7qfel0GjqdblILQLPZjEwmAwDIZDIwjZ26WmYymTAwMDDT\n/aUxZFFEMR5FMTSAQngQhXAIhuZWGNs6Jj9YZ4YKQNv6TeO3Uf7m5vpd16Cntw8v/uE1PP/Sy4gl\nkvj4nuvQuWYNAMBus+K7X598TcdyZjKZEN68CecDXZAa6tHZuW6pd4mIiObJSEedYqmEn7/4Irr6\nB7Cm82qs27gRZl9b5Rqmq9racez99wGgctu7J0/CoNejLzSII2++geBAPwqFAm7+2MfQ3NCI63bs\nwM3Xf2xp3hjRAplTy86patnGfsU23WNmyu8PzOp5tU4WRWAoDjkehRyPQU7GAVEE8tnKY4biUWi0\nehRm8L925P/Rtds2o6WxDuFoDM2NDXA5HOgJ9izEW0E+lwcA+AML/G2PxQLNZuUXnZFvpmj1WLTj\njFYtHmPzTyyWkOwLQ1ALcDSPLjxmA5MHYYk2DwDg2mv34QadrpIrurtHP7tK+QKSyST++N4x1JWv\nb3r/zIeIxuN4/Z130N7UjDtuvBkN5fabXV3dC/beZiufVybMr9b8Q1dOaG2set+cQr/VakWhUIAo\niuO6gmQyGVityqAni8VSWfUfa+xj6PLkUhFyIg4kyiF/KA5I8riQP0KCAGg0gMmGgqSaUTPWsb+U\nNdbVobGuNvsOy5KMRPmi4rEcdjtUAusuiYiWo1K+iERvCIneQSSCgxgaiEKWJNjsZmy+5bpxjx0J\n+RMZJ5QQj2UxmWE0GNHT31cJ/V+84y6Y2FmOVpk5hf62tjbIsoxgMIi2trbK7cFgEB0dSolJe3s7\nIpEICoUCdDrduMfs3j27biodHe1z2OvlSyrkUQiHKuU6xXgUkGVImXL7UxUAtQowmQC1FoLeAI3b\nC63bC63LC7XVVjMXFY2sinW0X6YUaZai0SjUr75aGT4GAKlsDpb9+9m9ZpVaiOOMaCweYzOXH84i\nEuhD2B9EOBBEvC8MWZJQSCoXzo4kBTFTQPOWnbOuChghyzK++9BDMBsnlxrXipEV/o6O9iXcC6oF\nXWK+6n1zCv07duyAXq/H4cOH8eUvfxkAkEwmcfToUdx///0AgL1790IURRw5cqTSsjMQCODChQuV\nx6xWYjaLYmQQhdAgCuEBlBJKD+BKyB+hVkZ4C0YTtK5yyHd7IZgtVxzyZcgInHofxXQaLTt2VCbt\nrjRWowEuy/jeyXKVxxIR0cLLDqUR6epD6JIS8pMDEQCohPwRKqPSAc7ssMHd2gRPqw+yJM9wjOhk\nKpWqpgM/0XyZU+g3m804cOAAfvCDH0AQBLS1teGJJ56AzWbDZz/7WQBKZ5/bb78d//AP/4B0Og2r\n1YpHH30UGzZswC233DIvb6JWiMOZSsAvhgZRSimlKNVCvtpshcbtqQR9tWn2/e79J06g7uIlmHU6\nnDl8GBv375/1tpYDWZYRi8XG3SbJEjh6iohoaWXiSYT9vQgHehEOBJEKKwta1UK+1e2Eu9UHT5sP\n7tYmGG0cekW0EGYU+idOsgOABx54AIIg4KmnnkImk8HOnTvxve99D5Yxq60PP/wwHn74Yfzrv/4r\nJEnC9ddfj29+85s1U4oyG7IsQ0ynUAwPKp11QgMQy+G+asi32qB110Hr8kDj8kI9j6vxxaEULHrl\nS1N1vvpXP7UiFosh/dvfVkp5Utkc8vv24fLVnkREtBBkWUY6mlBKdfxKyM/ElXBfLeTb69zlkN8E\nV4sPBgtX4YkWg0oeO22rBhw7dgw71i+/2klZliEOJcutM5WVfDE7PDngA0rIV6mgsTmgcXkqQV/Q\n6yc/dp6khobQ89JL0JRK0G/ciLbNmxfsta7E2DrYy63aA4DL5ar6i2E0GoXq8IuVUp5YOo3INbug\nf+MN1vRTBeutaaGttmNMlmUMhaJKwC/X5GeHMpMCPqCEfEGlgr3BC3err/JHZ6x+0S1dHmv66Up1\niXlcc801l71vTuU9q5ksSSgl4yiEBiur+VI+N0XIF6BxOCsX3Wpcbgha3eTHLhCrzYaNn/kMZMhQ\nYXl9wzJx1R5QwjpmGNadTgeE/fvH1fBboPzyQEREMydJEpL9YYTKK/mRrl7kM9nqIV8Q4PTVwd3W\nBHerD67mRmj1i/dZR0TVMfRfIVmSUIpHR2vywyFIxUL1kC+ooXW6oHV7oXF5oXW6odIs/X/u5Rb4\nR8zmAtxUNjfu7xaVwBV9IqI5kEQRsd7Bykp+pKsPxVy+asjXaDRwNjdUVvFdTQ1Qa5f+s46IJuO/\nzCpkUUQxGlZaZ4YHUYiEIJdKVUO+Sq2BxuUercl3uKBS87LSheJyuYAxq/pc0ScimjmxWEK0p1+5\n6Nbfi2h3H0qFYtWQr9Vp4WoZLdVxNNZBreFnHVEtYOgvk4pFFKNhJeCHBlCMRiBLYvWQr9VWuupo\nXF5o7A6o5thLeDUbu2o/8vNU/RtUKhVX9YmIZqiULyDS3VfprhPr6YdYEquGfJ1BD3drUyXk2xs8\nc+6bT0RLY9WGfqlQQDESKl94O4hSNAJZlqqHfJ2+Uo+vdZcHYfHENy8mrtoDXLknIpoPhWwOkUBv\nZSU/1js4bhDWWCqjGQazadxFt7Y694rutEe0mqya0C/lc+j90zEMn/kAKqkEm8UMQK4a8gWDcbQe\n3+2B2lI7025rDVftiYjmRy49jHCgfNFtoBeJ/jBkWa4a8k02ixLw25rgbvHB4nbws45ohVqxoV/M\nDpdLdZRynUI8ipw/AKtKRlGSkLHZYLRYRqfdmizQuse0zzSZeeIjIqJlbTiZRiQQrHTXGQpFAVTv\nkW9x2sf0yG+EycEFLaLVYsWEfjGTVsJ9+cLbUko54Y2s5IuSBIhFAAJUkoSS3gTDmg3KRbduL9Qc\n0U1ERMuYLMvIxIfKg7CCCAd6kY4mAFQP+Tava1y5jtHKabdEq1VNhv6RabeF0EBlNV8cnnrarc7p\nQM7sRCwxBMlbj/U33czVDSIiWrZkWUYqHC+X6ygr+cPJVNVSHRUwfhBWSyP0Zi5oEZGiJkN/+Nln\nIOWyU0+7tY8dhOWBoNPBufi7SkREdEVkWUZyIFJZxQ8HepFLTTHtVhDgaKyDu6UR7rYmuJobOO2W\niKqqydBfioZHf1BrAUGAxuEabaHpdEPQapduB4mIiKYhSRLivSFEAsFKd51CNlc15Ks1ajh99ZUW\nmq7mBmh0/KwjoitTk6FfpTNA4/QoF966lJDPQVhERLSciSUR8d5BhC71IBzoRbSrD8V8Ycppt66W\nxkq5jtNXz2m3RDRrNXn2cP35XQz5RES0rJUKRUS7+5Wa/EAvot39EIul6tNu9TqlVKfVB3drExyN\nXgj8rCOieVKToZ+Bn4iIlptirjzt9lIQ4UAQseAgJLH6tFu90TDaI788CIvTboloodRk6CciIlpq\n+eEswhd6kAgO4kLmLcT7wlNPu7WY4CnX47tbfbB6XewiR0SLZlWEflEUETh5EmKphI6dO6DV8MIn\nIiKamVwqg3CgF6HySn5yIIJUOoVSKgOzabQ15kiPfLPDNq5HvtlpZ8gnoiWzKkL/xbffRls4DLUg\n4PwfhnD1Lbcs9S4REdEyl0kMIezvrUy8TYXjAMYPwioND0PWGqAymmFxOSrTbt2tPpjs1qXadSKi\nSVZF6JcyGejLHQ+EbHaJ94aIiJa7l3/8DML+IICppt26YTVqYffVYcueXTBYOAiLiJavVRH6Pdu2\n4swbb0CQAds11yz17hAR0XKXy1bCvspohqBSjZ922+qDzmiA3x8AAAZ+Ilr2VLIsy0u9EzNx7Nix\npd4FIiIiIqJl6ZoqC9w1F/qJiIiIiGhm2BCYiIiIiGiFY+gnIiIiIlrhGPqJiIiIiFY4hn4iIiIi\nohWOoZ+IiIiIaIVj6CciIiIiWuEY+omIiIiIVjiGfiIiIiKiFY6hn4iIiIhohdMs9Q7Qyvf222/j\n0Ucfxblz5+B2u3H33Xfj3nvvhSAov3MeOnQIP//5z5FIJLBz505885vfxJo1a5Z4r6nWTHWcnT59\nGp/97GcnPeeee+7BQw89tAR7S7Xkj3/8I770pS9Vvf+VV15BQ0MDnnjiCZ7LaNau5DiLRqM8l9Gs\nMfTTgjp27Bi+8pWv4I477sDXvvY1nD59Gj/4wQ+gUqnwt3/7t3j88cfx5JNP4sEHH4TP58OhQ4dw\n8OBBPP/887BYLEu9+1QjpjvOPvroIxiNRvz0pz8d97y6urol2mOqJZs2bcIzzzwz7rZcLof7778f\nmzdvRkNDA370ox/xXEZzciXH2ZtvvslzGc0aQz8tqEceeQT79u3Dww8/DAC47rrrkEgkcPToUWQy\nGfzkJz/BfffdhwMHDgAAdu3ahZtuugm//OUvcfDgwSXcc6olUx1nAHD27FmsX78eW7duXcrdpBpl\nsVgmHTv//M//DEEQ8P3vf5/nMpoX0x1nKpWK5zKaE9b004KJxWI4fvw4vvCFL4y7/e///u/xs5/9\nDCdOnEA2m8XNN99cuc9ms2H37t14/fXXF3t3qUZNd5wBSujv7Oxcit2jFejChQt4+umn8Xd/93dw\nOp04efIkz2U07yYeZwDPZTQ3DP20YM6ePQtZlmEwGPA3f/M32Lp1K66//no8/vjjkGUZgUAAANDa\n2jruec3NzfD7/Uuwx1SLpjvOAODcuXPo7+/HXXfdhc2bN+O2227Db37zmyXec6pV//Zv/4aOjg58\n/vOfBwCey2hBTDzOAJ7LaG5Y3kMLJh6PAwC+/vWv44477sA999yDo0eP4tChQ9Dr9ZAkCTqdDhrN\n+MPQbDYjk8ksxS5TDZruOLvzzjuRSCTQ3d2NBx54ADabDc899xy+8Y1vAADuuuuupdx9qjE9PT14\n5ZVX8N3vfrdyWzqd5rmM5tXljrPBwUGey2hOGPppwRSLRQDADTfcgAcffBAAcO211yIej+PQoUP4\n6le/CpVKddnnVrudaKLpjrMDBw7g3//939HZ2Qm32w0A2Lt3L0KhEH70ox/xg5Jm5Be/+AXsdjv2\n799fuU2WZZ7LaF5d7jhzOBw8l9GcsLyHFozZbAaghLGx9u7di+HhYVitVhQKBYiiOO7+TCYDm822\naPtJtW264ywSiWDv3r2VD8kR+/btQ09PD7LZ7KLtK9W+l156Cbfccgu0Wm3lNp7LaL5d7jjT6/U8\nl9GcMPTTghmpbx1ZiR1RKpUAAFqtFrIsIxgMjrs/GAyio6NjcXaSat50x5koinj66adRKBTG3Z/P\n52EwGGA0GhdnR6nm9fX14dKlS7j11lvH3d7W1sZzGc2baseZ3+/nuYzmhKGfFsy6detQX1+PF154\nYdztr776Kurr6/GpT30Ker0ehw8frtyXTCZx9OhR7N27d7F3l2rUdMfZwMAAvvOd7+C1116r3CfL\nMl588UXs2rVrsXeXatipU6cAANu3bx93+44dO3guo3lT7TjjuYzmSv3tb3/720u9E7QyqVQqOJ1O\nPPnkk4hEItDr9XjmmWfw9NNP46GHHsKOHTuQTqfx4x//GAaDAbFYDP/4j/8IURTxT//0T9DpdEv9\nFqgGTHec3XrrrXjrrbfw7LPPwuFwIBwO4/vf/z5OnDiBRx55BF6vd6nfAtWIF154ARcuXMC99947\n7nadTsdzGc2basdZU1MTz2U0J7yQlxbUXXfdBa1WiyeeeAK/+tWv0NjYiO985zv43Oc+BwB44IEH\nIAgCnnrqKWQyGezcuRPf+973OMGSZmS64+zQoUN49NFH8dhjjyGRSGDTpk146qmnsHHjxiXec6ol\nsVisao0+z2U0X6odZ4Ig8FxGc6KSRxpZExERERHRisSafiIiIiKiFY6hn4iIiIhohWPoJyIiIiJa\n4Rj6iYiIiIhWOIZ+IiIiIqIVjqGfiIiIiGiFY+gnIiIiIlrhGPqJiIiIiFY4hn4iIiIiohXu/wPY\nP2ek7WOz9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b498b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm, mesh=False);\n",
    "points_plot_prob(ax, Xtrain, Xtest, ytrain, ytest, clfsvm, prob=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision function can be used to plot the margins of the SVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#From Jake Vanderplas's ESAC notebook\n",
    "def plot_svc_decision_function(clf, ax=None):\n",
    "    \"\"\"Plot the decision function for a 2D SVC\"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n",
    "    y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n",
    "    Y, X = np.meshgrid(y, x)\n",
    "    P = np.zeros_like(X)\n",
    "    for i, xi in enumerate(x):\n",
    "        for j, yj in enumerate(y):\n",
    "            P[i, j] = clf.decision_function([xi, yj])\n",
    "    return ax.contour(X, Y, P, colors='k',\n",
    "                      levels=[-1, 0, 1], alpha=0.5,\n",
    "                      linestyles=['--', '-', '--'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can also access the support vectors...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 225)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l5TiFhfmZTisnyW88IYQQW0ZhoZ/PfMbD8LBGWdnC6/mFWI6qqvh8hSlr+MXa+HxuEolR\nrNYKFGUMq3XrTW7eLFL0CyGE2FIsFgtWqyXTaQghgJKSIpqb7zE4eIPa2m0YhrwZ3yhS9AshhBBC\niIypqtpGVdU2QOYYbCQp+oUQQghhCoZh0N7eRSBgUFPjpL6+KtMpCZEzsrLol+Fc2RFT8jFfTMnH\nfDElH/PFNFs+y3nYmIZhMDExjs3mwGq1riifjToHfX13uXFjGzabhzNnenG7ozgcTlP+TLb669Js\n+WQiptnyWU5WFv0ynCt7Yko+5osp+ZgvpuRjvphmy2c5a33eggKDd9+9wuhoERZLPx//eDVut3tF\n+WzEnzMcBrfbwG4HRTEoKACnc/nHbVQ+Zosp+ZgvptnykeFcQgghhEgTi0UZGfHhcm3Dam2gp+de\nRvOpqamgsnIQq/UGhw7lYbfbCQRGCYVGCQSS/+m6ntEchchWWXmlXwghhBAPz2azY7eHSCRKmJy8\nx7Ztme1BqSgKjY27Zr4PBEb5+c/DaJoXtzs5BOvkSSgoeIiPRYTYoqToF0IIIbYoi8XCJz6xi97e\nexQX+ygsNN+AKYfDCxSSL/OahHgoUvQLIYQQOUTXdUKhIAChEChK8nafz4+qpq/qtdvtNDTUbGaK\nQogMkKJfCCGEMLnJyUna2m4Sj6vs3eunsrJs0fuGQkF+/vMwDoeXSARZFiOEAKToF0IIIUzv4sVe\nJiYaUFULHR3Xliz6IbkkJj8/WeBn+7KYWCyMps1+Dd6M5iNEtpKiXwghhDA5i0VB1zVU1YKqGuv2\nvHOXAk3z+fyYpbmfz+fn5EkIBsHvB/A+yE8IsVpZWfTLcK7siCn5mC+m5GO+mJKP+WKaLR+A7dt3\nMjx8k1jMYM+ebSn/Ds5/bCgEkUjy6+CDej4SSX5tGKmPC4WCvPlm+MFm2eSV9BMnIB5fehnQ5p0D\nFSgkHp/NPRhMv9dWeB1IPuaLabZ8lpOVRb8M58qemJKP+WJKPuaLKfmYL6bZ8gELJ040rOixigIW\nSxiABwN2sVjC+P1eCuZ15FQUKCqaXQo0Pp68om4Y5jsHZssnEzElH/PFNFs+Sw3nysqiXwghxNZg\nGAaTk5OAPdOpZI3pJTGw8ctiFloeFAqB379wpyCRPGeBQPrHFWZaViVykxT9QgghTCkej/PWWzcY\nHfWwc+cEjz66N9MpZQVVVWe69BgGaVf350tujp379co3ys7tFDRtZCTMCy9Ip6DFjI0FOX069ZxN\nd1cCOWdi40jRL4QQwpR6egYwjAacThuDg3eIRMZxu7O8FY3JzP1UICn5icBC6+YXM7dTEMzuJxCL\nm3/OhNgMUvQLIYQwpaIiD9euDWEYFVgsYez2pdtUitWb+6mAECK3yeIxIYQQplRSUsTx4zZKS2/y\n9NNV5OXJdSohhFgr+Q0qhBDCtMrLS7DbS7J+wFQum7snYPZ7GaC1FDlnIhOk6BdCCCHEmqTvCYBg\nUAZoLcXjST9na9lLIcRqZWXRL8O5siOm5GO+mJKP+WJKPuaLabZ85tJ1nbGx1MpwaAhqaxdukbnx\n5yA5PGuueHzhAVqbk4/5Yw4Pq5SWpu+jCAaz6xyYLZ9MxDRbPsvJyqJfhnNlT0zJx3wxJR/zxZR8\nzBfTbPlMCwTS2z2OjISpr1+8RWaunAOz5ZOJmJKP+WKaLR8ZziWEEELkiFxtkdnff48PPghht+u0\ntu7E4XBkOiUhcop07xFCCLElxGIx7ty5Rzwez3QqYgFXrgRR1d3E47u5eLEv0+kIkXPkSr8QQoic\nF4vF+Kd/ugVsx2K5ybFjuwBbhrN6eLquMTkZxTA276p4IBDi/fcHKCpSOHasFqvVui7Pq6o6ALo+\nhc0m1ySFWG/yt0oIIUTOGxkJoiiVOJ0eEolyxsbCyz/oIQ0ODtPd3Y+maev6vLFYmPHxUQKBAd5/\n/zLnzw/R1nYdwzDWNc5izpy5SyKxh1Cojo6OnnV73qNHt+N0Xqe0tJcDB3as2/MKIZLkSr8QQoic\nV1xcgKLcJBIpx26/i8fTsKHxenrucP68BYulgL6+6zz55L51ed65LTI7O++hqtuJxfKIxSaIRidw\nudzrEmcphqEAoCgq6/l+xu/3UVvrW78nFEKkkKJfCCFEzrPb7XzqU7sYGwvj9TYwNrY+S1IWMzg4\ngctVD0A4vH6xVFWd6dKza5fC3bth7PZybLYB7PbydYuzlKamEt5//xoOh8GRIzvTjmuaxvh48g3I\nQm1EhRCZIUW/EEKILcFms1FUVLwpsXbuLOSXv+zCMNxUVW3MspuiogKeeAK6uu5y+HAdFotlQ+LM\nV1ZWxFNPFS3YNnBycpK3376BzVZEfv5tnn56z6blJYRYWlYW/TKcKztiSj7miyn5mC+m5GO+mOuR\nT15eEcePu0kkJsnPL2d0dGWPu39/kKmpKSoqylEUZelEAEUpwOcrYGICJibWlutqjy11/M6dYUZH\nd1Ba6mZgwMrt2yF8vuS7A13X6e4OUlIye3+PZ3aoWDa9RjIRU/IxX0yz5bOcrCz6ZThX9sSUfMwX\nU/IxX0zJx3wx1ycfx4P/Vva4+/d76ex0oyj5RCKdtLTsXjqJVeezfscWO26zeTl/fgCHo5q8vCG2\nb6/F9qBJUiAQ5Pz5MEVFycFisViYkydTh4pl02skEzElH/PFNFs+MpxLCCGEMLnh4UmczhoAQqH7\nGc5m5cbHxxkaClFRUUx+fj6PP15GPD5AZWU1NltqW9T5g8WWo+s6oVCQUAimP/jw+fyyV0CINZCi\nXwghhDCB6up82ttvAVbq6rJjHfzY2DhvvjlAXt52Ll/u4tlnd+PxeKip8azL84dCQX7+8zCa5sXt\nXvjTASHEykjRL4QQQphATU05paUTaJpGfv7KiubpK+Fzl/9v5pXwwcEANlsVVquDSKSEiYkIsL5t\nNx0OL1BIfv66Pq0QW44U/UIIIYRJOJ2uVd0/FAry5ptLr5PfSBUVxVy50s34eAn5+YO43XsJhRa/\nf3Kw2OzX4N2UPIUQUvQLIYQQWW216+TXk9Pp5FOf2sXERASPZ++S7Tl9Pj8nToDfP32LF5/Pv+j9\nhRDrS4p+IYQQQqyZzWZL27C7EFVV8fkKKShY3fPHYuGZyb/y6YAQaydFvxBCCGEy8XgcRVFWVExP\nL5nRdY2bN7spKbHQ2uolL29l/8QHAkEikSjl5aWmG6Tl8/k5eRKCwelPCOTTASHWKiuLfhnOlR0x\nJR/zxZR8zBczl/Lp7h7jzTdvMzWVx549+ezYsX3NMaempohEwrhcHgIB65ryWc9juq4zNhYEYGgI\nSkpSB0utZ8yenj6uXp1CUXQOHnRRWVm+6ON03c/hw8l82tu7KSvbwd27fk6dusmxY7uXjTkwcJ/2\n9klU1YfXe52PfWzfsrmu5s/y8MdUoJB4HIwHg42DwUzmk7mYko/5Ypotn+VkZdEvw7myJ6bkY76Y\nko/5YuZKPh99NIDfvx+Ae/eu09S0tpiJRII33+wkFivDZuvk6NF6Cgtnr3hPd6wBsNuT/dsX61iz\nXucgEAhy+nQYh8NLJAJdXYtvmH3YmGfPRiktbQBgdLSTQ4cWL/pBRVULKSyEnp4CPJ4yAPLyZp9v\nqZg3b4YpK6sHIBazUVBgzEwCzpXXZS7FlHzMF3Oz89E0jYGBSyiKwr59h9KOy3AuIYQQG87lshAK\nRbFY7Nhs2pqfJxgMEo9vx+32E43aGB0NUFZWNnN8unf7dAFusaQX4Hfv3mdkRKegYNtMEfuwNmvD\nrNdrMDQ0gWFolJauvPXm3r0FnD17DYCDB4tW9JiamkL6+28CXrZtm1q3cyWEWF8TExHOn/+I06c/\nxDDGyc/3snv3/lUtyZOiXwghxLrYs2cn9+/3Eo0m2L9/55qfx+v1oqrdxOM24C5+/460+yxVgF+6\n1EN3t49IxEIsdoNjxxrWnEsmHDtWT3d3P6qqsnNn3ZL3ndun3+Gw8LGPlQKseN17aWkRzz7rIhaL\n4vfvfujchRDra2pqilOn/hdXr15E06aIRsHphPHxMLdu3aSubuW/36ToF0IIsS5UVX2oYn+a3W7n\nmWd2cP/+KGVlNUSjjlU9fmRkCoejkKkpCAQWXgAbj8f4xS9uMTUFR45so6jIHJtDp4v4oqLkJKpg\nMLDksK35ffph9b36nU4nTqdzVXlqmsa773YSDudRWaly+PDSb06EEGuTl5fHyMgQmjYFgKIo1NU1\n0NTUQlXVjtU91wbkJ4QQQjwUp9M5sxE4Gk0/nmzdyMzynrltHKuqXJw/30skYmHPnoU3AV+40Ith\nNKCqKmfPXuWTn1y+6F8q5nqZu3RpOuZyBXwm+vT39d0lGKzBbnfR03OHXbvGcbtlZK4QG6GpqYV/\n/uefcuBAIzt3HmfnzrX9fZeiXwghRFaZbuMI060cU9s41tZup7x8gpERncrK9SlEl4u5njI5bGul\nnE47uh4BXECUvLziTKckRNYKBgN0dHyAzWbn0UefSjteX7+XHTt24XA4FuxguVJS9AshhMgqqqrO\nXPk2DBYc9uR0unC5Fn+Ow4d3cONG54PlPRXrEjNTpvv0z/1+owdYlZWVkpd3m+HhGxw86Mdut29o\nPCFyjWEY9Pff5uzZNm7evIZhGNhsdpqbW4HUv08Wi2VdZmhI0S+EEGLD9Pff58aNEJoGTz9dZ5rh\nTzabnccf37PgMV3XCQTSm8EvtbZ+PU0vI5r9evEC3ufzc+LE9OCqaZszwKq+vpr6+g0Ps2p37tzD\nMAy2b1+/zk1CrKepqSl+9KPXGR+/m3b7wEA/Xu/G7JHJyqJfhnNlR0zJx3wxJR/zxczlfDRN4xe/\nCOF0NjAyEsftvs3u3ekbfc12Drq7g5w/P7uuHpLF94kT4PMVbmg+uu6ntXXuES+a5l/iI32VeLxw\nZnDVtOkBVmZ4HWz0sbnHL1++SV9fIYqism3bTQ4f3rXlzoEZjm2VmGvPJw9N8xKNJot+p9PN/v1H\nOXDgGC5XfuaGc+m6zhtvvMEPfvAD7t27R0VFBV/4whf44he/OHOf7373u/z93/89wWCQpqYmXnrp\nJWpra2eOT05O8sorr/Czn/2MiYkJHnvsMV566SVKS0vXlLQM58qemJKP+WJKPuaLmav5aBrk5yfb\ny01NKXi9Rlb8/i4pgaKi1HX14+PJq+nTy3o2Lh+V4uLVr+c38+tgM/PRNJ3i4uQPSdMGVzSgLNfO\ngVmObZWYy+Xj82kLfsL5+OMtnDsXpKmphT17DpCXl1qSrzXmUsO5lv2c8q/+6q/4sz/7Mz772c/y\n3e9+l5MnT/LHf/zH/Lf/9t8A+Mu//Ev++q//mhdffJFXX32VsbExvvzlLzM+Z4HhN7/5TX7yk5/w\nH//jf+Tll1/m+vXr/M7v/A66ri8XXgghRJayWCw0NnqxWDrx+3vYs6cm0ymJHFdV5SQSuU0k0kdV\nlewzEJmh6zpdXdf4h3/4Pv/0Tz9Z8D4VFTX85m9+hQMHjqQV/BtlySiapvH973+fF198ka985SsA\ntLS0MDo6yuuvv87nP/95/vt//+/8/u//Pr/xG78BwNGjR3n66af50Y9+xJe//GVu377NT37yE/70\nT/+Ukw9aH+zZs4dnn32WU6dO8cwzz2zwH1EIIUSmVFdvo7p6G6OjYJLl/CKH1dVVUl4ewTAMaSEq\nNl08HufSpXOcO3eGUChANAqhkMoTTzxDfr4n5b6KorDZW06WvNIfiUR47rnn+OQnP5ly+44dOxgd\nHaWtrY1oNMrHP/7xmWNer5djx47x7rvvAtDW1gbA008/PXOfmpoadu3aNXMfIYQQwkySHXFGZ/6b\nu7lWmJvL5ZaCX2w6TdP43vf+K//6r/+bUCgwc7vH4yMUSm8MkAlLXun3er289NJLabe//fbblJeX\nc+/ePQCqq6tTjldWVvLWW28B0NPTQ0lJCQ5H6kTFqqoqenp6Hip5IYQQYr15PLM9+WdtTkccIUR2\nslgs1Nbu5uLFswBUVu6grq6FpqaGTen6tRKrXkT0wx/+kNOnT/ONb3yD8fFxbDZb2lokt9tNJBIB\nkp8WuBZoluxyuWbeNAghhBBmMbcnv1gfgUCIDz+8y9gYPP10OYWF8gZKZKepqSkmJiJ4vb60Y83N\nLei6RlPpYfT6AAAgAElEQVRTC6WlyWWNJqn3gRVs5J3rpz/9Kd/85jd59tln+eIXv4hhGIv2wJ1+\nV7OS+wghhBAit0SjUQKBUQzD4Ny5AWAvirKXc+fkgp/IPpHIOO+//zZ/+7d/tujm3KKiEp599rOU\nlm7b5OxWZsVX+r/3ve/xne98h0984hO88sorAHg8HiYnJ9G01HZEkUgEjye5YSE/P3/mqv9cc+8j\nhBBCiNwxPDzKe+8FMAwfxcVXsVpVJid1dB2s1kxnJ8TKDQ0NcOZMG9euXULXNQBu3+5meHgQVV1b\n6/lMWVHR/+qrr/Laa6/x3HPP8e1vf3vmCn1NTc2DMcL91NTMtmLr7+9n587kAJYdO3YwPDzM5OQk\nNpst5T7Hjh1bU9IynCs7Yko+5osp+ZgvpuRjvphmy2c5ZjwHd++OoOvJcb23b4d56qltXLp0A02D\n+vodq/533Iw/k63+ujRbPhvxvJqm8T/+x/+DxTJ78VpRFHbu3EMwqDA1tbn5LHdsOcsW/W+88Qav\nvfYaX/rSl/j617+ecqyxsRG73c6//Mu/8OKLLwIQCoX44IMP+OpXvwpAa2srmqZx6tSpmZadt27d\noqura+Y+q5UNw13MmE8mYko+5osp+ZgvpuRjvphmy2c5ZjsHPp+ftrbbqKqfiooo27e72L59N6Oj\nufUz2eqvS7Pls/7Pa+H48aNcvvwLbDY7Bw82ceTII/j9yQFwy72e1z+f5Y8tNZxryaJ/cHCQV155\nhYaGBj796U/T0dGRcvzgwYP8xm/8Bn/+53+OqqrU1NTw13/913i9Xl544QUg2dnn2Wefndn46/F4\nePXVV9mzZw8nTpxYKrwQQghhWrqup7Xi03U/q9wul5PKy0v4+MfDjI9PsG3bnkynI8SSQqFRotEI\n27dXpR3bv/8opaUu9u8/gt2e3QPfliz633vvPRKJBDdu3OBzn/tcyjFFUTh9+jR/8Ad/gKqqvP76\n60QiEZqamvjOd75Dfv5sj9yXX36Zl19+mVdeeQVd13n00Ud56aWXFt3gK4QQQphdKBTk5z8P43B4\ngWRv/9ZWKC6Wzj8APp8Xny95bqbfIIVCpAwk8vn80tRDZIRhGPT13aK9vY3LlzuprCzl3/27302r\nTd1uD1VVxzOU5fpasuh//vnnef7555d9kj/8wz/kD//wDxc97nQ6+da3vsW3vvWt1WcohBBCmJTD\n4SU/X4r85Uy/QdI0L2538rZYLMzJk0h7VLGpdF3nypXznD3bxvDwfQAMA4aH79PXd4vq6p0ZznDj\nrLpPvxBCCCHEaiU/ESkkX4bligxSFIUPPniPQGBk5jaXK5/W1mMUF2dXN57VkqJfCCGEyIC5ewJC\nITAMHUidYbOW5S/37g3T3T1KaamTwsL0NcpCbGWKotDYeJy33voZpaXlNDW1UFKyn9LS3C+Jc/9P\nKIQQQjyEmzf7GB2N0dBQNrNGfVosFp73tZeVmrsnIBKBeLwfyKOwsGLm+Va7/CUej3PmTBCns4Gh\noSEaGgYpLMztq5dCzKfrOl1d19A0jb17D6Yd37//CCUlZWzfXo2iKAu2kM1FUvQLIYQQi+jpucPl\ny24cjireeecKn/nMHqa78/h8fh50on7Ai6b5V/X8qXsCAihK3kPtEZiaSmAYTgBU1c3ExPCan2u9\nxWJhNC31+9W8SRJiOfF4jA8/bKej4wPC4SD5+V4aGvalDJAFsNlsVFbWLPIsuSsri34ZzpUdMSUf\n88WUfMwXU/IxX8y5x/r7o0xObmNqCiYmnAwNTREITA+aVIHUAn14GBZbjTM/ZigE0wPrg0FIJJJf\nj48n/x+JJG83jMVzTX/efPz+e9y7dwO3O4HDsXvRq5ib+TPRdT+trTA0BCUl07cm3yRN55dNr5FM\nxJR8lh6i9f77/8TZsx3Y7ZMzt0ejYc6d66S2dm/OnIPoYBhILB14EVlZ9MtwruyJKfmYL6bkY76Y\nks/mxtQ0jVhsksJCJ729A1y4EMJi0Wlt3U5BgS/lcY88UsUvfnGNaNTGwYMqZWU2rNa15RMKhfno\noz4mJsIcOVKOoiQve7tcyd7+ySU+Y0CyCrZYwvj9XgoKFo+1UMynnto187V5hmGpFBcX4vOZJZ/s\njCn5LD5EKxodwG6fxJn8oIvq6lqamlqora2faRObC+fAzjhFGzGcSwghhMglkUiEt97qJRTy0NAw\nwcgI2O3J4VGXL1/nscd8Kfe32+188pP7MQxjwdky9++P8OGHg4BCc3MRdntJ2n2mXbx4l6mpctrb\n3Vy6NMaOHV4++miQo0cBClAUhRMnnPhmUvDi861uuZAQW1VTUwu3bg1w8OAhGhuPU1JSlumUTEeK\nfiGEEFtGZ+ddbLa9uFwKAwO3cDiC6LqOpiVwuy2LPm6xYZKXLw9js+198PV1mpoWL/otFkgkDKxW\nDy5XHiUlFRw7Bo89NoVhgN/vl2FVQixibCxMW9uH+P0WHn30qbTj9fV7+c3f3EFlpXvzk8sSUvQL\nIYRIMbeV5FzJq87ZXZCWlHi4ffs+ul6C0xnhiSd2c+lSFzabhQMHVj+Ux+mEQCCBoii43Usvvm9s\nrOHixfPY7W7q6xtQFBWXy4PPB4ZRuKJlPNlofmvS6fdP8gZHrMTAwB3a29vo7LxMJKLj89lpbm7F\nbren3E9VVVyurVnwG4bBzVu3SExNsdQgDCn6hRBCpJjbSnLadPvI+RtXs01l5TZUdZDu7lscPVqH\nw+Hg2LGGNT/fsWN1XLp0C8OAAwfqGBtb/L4Oh5NHHmlgZCRMIhEhkYisqINNPB5HVVWsVuua88yk\n+a1J3W6ZxiuWp2kaP/rR39Hf35tyeyIxSX9/L3V1a/97mysmJye5cPUqbe3tDI+O4vd6efxXfmXR\n+0vRL4QQJmOGK6OprSRzS0VFKQ5HKQ5H8vv5n2xMn/OVnO+8vDyOHNm15H3mWqjNp8/nJ5j+wQoA\nV6/2cv26gaIkeOQRP+Xliy8fMrO5ryeZyCtWwmKx4HC4Zr632x3s3t3M448f2/J7XULhMB92dHD2\n4kWisdjM7cFweIlHSdEvhBCmI1dGN9f8TzYikWTnnI0436qqruo5b92K43Ilr2jevNmZtUW/EEvR\ndX3BN9jNzS2MjAzS1NTCvn2HGR+3zdnovvXcvdvPxXd+Rt/ADXRdTzlWVVHB8cZG4ks8PiuLfunT\nnx0xJR/zxZR8zBdzoWOhEGiaFyic6d2uaek92zcqH7sdRkbCMz3kIfmmIxj0El/iX5Rs/ZnMPd8w\n2y9/7vmOxaKcP9+LYUBFRQ3gXHHMu3fvEY3GcbkqgYU3Cy+Wq2FoBAJxdD1Gfr511f/+meFnEgrN\nvp5CIfD5Zl9P6/F6fpjHZlPMXMvHMAz6+7s5f74Nu93BM8/8etrjnM5qfu3X/i8URWF8PLfOwZL9\n9gc1pn9XaJpG583rtF/4iHuDA1giYVzu5BskVVHYv2sXxw8epHLbNgDOLvFLOiuLfunTnz0xJR/z\nxZR8zBdz/jFFSV7dn14GMf1/v5+0zZ4bkY/f7+eFF+bfOrsMJdd+JvPP97S55/udd3pRlAZUVaG/\n/zpHjuxZUczOztt0d+ejqj4UpZP9+/euKtdPfaqBzs7bOBw2du5cfKOxmX8mc19PwWDyvE6/nuZf\n3DXra8QsMXMhn0QiweXLF2hra2NkZAjgwZ6VZ/B4vPMel941KxfOASzdb7+CUSacTs5euMCHHR2E\nH0zs8zgBVJwFBRw9dIhjR47g9XhSH7xEo/6sLPqFECLXJTd4zk5sXcmGz/Wy2iUouSi5zj808304\nHGRyMojL5UfTVv48w8NxHI5qAILB1f+Ta7FY2Lt39V2FzGTu68kw0t+4iq1D13W+972/ZHAwNDNE\nC8DpdBMMjs4U/VvZ4PAwZ955h/O3bzM175dNSVERLXV1HGppWdPGfin6hRDCZOZu9px/ZVRsjOk3\nWcCDybj9vPlmHoWFyQo1Gq3k6tVrHD2qcujQ4lfr56utLeDMmRvouoOKisXnAAixFaiqSm1tA4OD\nHwKwbdt2mptbqa/fi8Wydf9+GIZBV08Pbe3t3OzthWiUue+K6nfupKWpidqaGpRAANbYyUuKfiGE\nMBm5Mrq55nfUCQZBUTycPeub03GmEKfTxZNPgmGsbCfhtWu9jI7GeeQRL36/l2jUtfyDhMgBmqYR\niUxQWOhJO9bUdJyhoQmefLKViorKDGRnHpOTk5y/coW29nZGAoGUY9a8PI7s38/xpiaKl1tDtEJS\n9AshhNjS5i9nMozkOv9FhvCuSE/PHTo7fTgcfj744Dqf+Uwx0eg6JCuEiUWjUS5ePMu5cx9gsxXx\n7//9l9LuU1hYzKc+9W+XXQufy0KhIJd/+Qu6bl0kNm/jrc/j4ZEjR2h69FGczoUbBqyVFP1CCCHE\nOotG4+TlJdtrGoYdTdOQf3JFrhoZGaK9/QxXrpxnairZkSYaDTM0dJ+SkrIMZ2cOhmFw924fZ8+2\n0dV1FXUijGtOTV9VUUFLUxN76+tRg8GU5T3rRX4DCSGEyCoLDS9b6eCyRCJBJDL+oEXn0muI567z\nn/3ei67rBAKp07Tmx6+vr+L+/RtMTOSxa5cVu92e0gJ1PS2UD4DH4yUUCqd8YrGZA97E1qDrOj/8\n4RtEIuMpt1dV7cKY25N1i9I0jevXL9Pe3sb9+3dTjqmqyoHduzne2Mj28vINz0WKfiGEEFll/vCy\nuYO0BgYGiccTVFeXA6nFbSwW4803u0kkStH1q3z2s4tvHkyfnAvTm6l7e4O4Tv8Yz4NpoWOxCUIn\nn09ZImS1Wnn66X3r+KeeFY/HCYfD+P1+rFYrY2NBTp+eHS4GyTcojz4a4M03LRQVeWdukwFvYr2p\nqsqhQ0c5ffpfycuzsm/fYZqajqMoJVt6Cc/ERISPPjpLd/eHRCJjKcecThfN+/Zx4vEjeDZxRHVW\nFv0ynCs7Yko+5osp+ZgvpuSz+pgLDS8LBqGzs5euLjeq6uX69U5qalJ76ff2DjM+vhObzcm9ewZ9\nfWN4vekdkZLxVKaHdc0VDMLQEOzUXFhJblJU5wxO26hzABAIBBgaGqarKwZUYrN18tRTuxkaSh0u\nBslhbqFQgHh89vb5A97OnbuD1zvFzp1VaVf/zfY6mH9M13XGxmY/3RgagpIS8HjSP8nIlb8Lmc5n\nbCzExMQ4ZWXb045VVx9lYiKPffuacTicm5LPZj3vSodoTRsaHuTcxbNc6bzCeDiB0z37UVtRYQnN\nh5rZU7+P/MA9PJOTqy9qV/LLYhFZWfTLcK7siSn5mC+m5GO+mFstn2AwzJkzd9B1hcbGYkpLi1cV\nc6FhWn4/3L07SUlJDQC6bqG0NPVxNpuf3t5+LJbtFBcPsX17/aKd75bKp6QE3P2z8RNA3pxBXos9\ndmxsjIsX72C1Khw9uhObzbbimHfvDnLhQpxAwM7AQJBjxwqIRHSs1jAlJdDfnz5czOdL/jf/PBUU\nwIUL3QwOFhGL2UgkOnn00fRhY2Z+XQYCqZ9uRCLQ1bX4Jxlm/buQDfnEYv20t7dx48YVCgqK+dKX\n/g+UB2vGph9XWJhPZeVjm5LPZp+D5YZoUViIYRjc6O6mrb2d7tu3AXDZwOVOtt5sqK2lpamJndXV\nD86dDta8jTkJMpxLCCGEWVy4MICi7MVigQsXrnH0aPG6PG91tYePPuoBbNTVpS/byc/P58SJcoaG\nAjgctWsabvMwzp7tJz9/L9GoRnv7TVpaGlb82IGBEG53PVZrnO7uXsbHA9hs98jPbyAYHFtk/0Hy\n/w+GeaYMeAsEprDbfVitMDaWnWv8HQ7vTEtVsb50Xaez8wrvvttGONw/c/vIyCC3b/dQU1ObwezM\nYzKRoOPcOdra2xkNpu6rsVmtHKmr4/gTT1BkknVOUvQLIUSOmrvhdVpywFdmizybTSESmUJVLVit\na9voN3discWSLGarqrZRXBxF06bIz/cs+Kl5fn4++fn5Cx5bjbHYRMrXtiXuO03Xk1dHFUVF11f3\n5965s4S+vk4Mw8fHP57P7t1xSkrqsVqteDwL7z/weLycOBF+MNwtedv0gLe6Oh/9/V0oSh779jmW\njD05OUk0OoHX65u5witym6IonD79r9y/PzzTRMbhcHLwYDNFRSWZTc4EgqEQH3R00H7mDLF5+4L8\nXm+y5ebBgzgmJpb/aGITSdEvhBA5au6GV5jdyLnQWvXN1Ny8k46OHhIJncbGHavuX58+sXi2mF3v\nvtYL8Xj8WE4+z3R3bRusaFryoUOl3L59DVU1OHJkx6piFhb6OXnSSTwew+M5klJ8z58zMJfPV7jg\ncLfKyjJOnCjA59NxOBYv+sfGxnnrrX50vZDCwis88cQ+0xT+86coT7/5Ew9PURQaG49z587/oqio\nhKamFvbuPbTpn46ZiWEY3L5zh7azZ7l282ayM9Hk5ExrzZrKSo43NrJn167ZfSUTE0s84+aTol8I\nIXKYGZdAWK1Wjh2rn/l+tUV/picWL1VkL6W4uJCGhrX/LOx2O3a7fc2Pn89qtbFEvQ9Ad/c9JibK\nuHMnwo0b4zQ3j5Ofnz5ldbMtNEV57ps/sTzDMOjp6WJoKM7x4wfSju/bdxgo4PDhOtO80csETdM4\nf/kabe3tDMzbRGtRVQ7s28fxxkYqtm3LUIYrJ0W/EEIIIRa0fXsh//N/XsHhaELXh7hx4x6NjZkv\n+heaorzZb/6y1eTkJFeunKe9vY1AYARV9XD0aHr7WpvNRnX1roeaTJ3NJiYinD//EZdOn8IwUods\nuJxOjh0+zNHqajxVVRnKcPWk6BdCiBw2dwnE3I2cQqxEUVEB+/bZSCRG8PkaUJTFO4MIc9N1nffe\nO8WFC2eJx2Mzt0ciY3R1XWP37v0ZzM48hobu097extWrF9G0KSzRyMzk3LLiYlqamzm4Zw95eXkL\nt9s0MSn6hRAiR6UPmEoufwimD28VWW6jNm0risLjj9dw7VoQh2OcAwd2PdTzicxRVZWBgf6Ugr+i\nooq6uhbq6/dmMLPMMwyDrq5O2tvb6OvrSTmmKAq765ItN3dUVWX1UqesLPplOFd2xJR8zBdT8jFf\nzI3NJ33AVDC49ufUdZ3u7iAlc5p3zB2GtNbn7euL8t57t0gkFPbvL6K0NLU7SG79TNYWc7l8QqEg\nb76Zumn7xAmIxxffQ7DSfJzOUhobSwEIh1f32OWOjY+Pc/58P4YB27dXAgtPJzXjzyQbX5d1dS10\ndfVRX3+AgwePU1a2ncFBFr0QkE3nB1Y/SCs+GefytUu8/8sPmdRDKcesVhsH9hykpXwbu3YVJW8M\nBFaXkMl+WWRl0S/DubInpuRjvpiSj/liZks+gUCQ8+fDFBWldgOau7Z6+rHzrzzb7eD3p09LBfjo\no9vYbLtxOFS6u6+xZ096S0CznINMxlyKokBR0eym7fHx5CAuwzD3ObhwoR+7PTkY7O7dazQ3pw8J\n28x8zB5zuXwcjggXLpzFMAxaW59Me6zfv5vdu7+Gx+NNO7YR+Wz2OVjJIC2AQDCYbLl58SLxyUns\nehT7gy48fq+X442NNB44kOxsNTqaXSdBhnMJIYRYDyvtBjS/XejISJgXXlh4Wmpenko8rgMqFsva\n+vZvFQst49F1P5b0WWRZQVGSSyuyecmEGQwN3eedd87Q13cBTZvCarXR2Hg8rR2rqqppBf9WYRgG\nvX19nGlvn225OUdNZSUtTU3srqtb8OJELpCiXwghxIaY+wYhEln8fvv37+DWrW7icYNDh7ZvUnbZ\naaHZC62tyav62bhp++jRKj766DoAe/dmTxcUs9B1nR//+P+lt/cm0ehMy3gSiUn6+nq2/Fp9gKmp\nKS5du0bbe+9xb94vIouqcmDPHlpqaynfvTtDGW4eKfqFEEJklNVq5fjxhkynkTUW+rQlWzdtu91u\nnnwyuaQnyxqhmIKqqthss7MbrFYb+/cfobHxOIWFRRnMLPPGIxE+On+eD8+fJzIxwdx3RW6XK9ly\n8/Bh8t3uLfPik6JfCCHEisViYcbHZ7/OhqvJW8FaB4aJ7JFcjpK+DKq5uYX79+/S2PgIjz7atOSE\n5a3g3uAgbe3tXLx6FU3XU45tKymhpamJA9MtN7eYrfcnFkIIsSY+n58TJ5JLSZKWnoCajctN+voG\nGBiI09RUuSWLAmEuhmFw924fZ8+2EY1a+Nznfj3tPhUVVfyH//BVgkF12QnLuSrZWayTC+/8nOFA\nf8qxZMvNOlpqa6k5cGBL7x+R32hCCCFWRFVVfL7CFU0+nb/cJBhc+g2CGVy71ktnp4dotJBotJMn\nn9y35P0X2lQbCi3epWi9ZOObKbE6mqZx/fpl2tvbuH//LgCxmEo4fAKv15dyX0VRtmwhG4/HuXTp\nHOfOnSEUCmCJjs0M0rLbbDQeOMAjR45QWFCQXMKzRc/TNCn6hRBCrLv5y00MA8zeEGN0NI7DUcPU\nFIyPL//P4/xNtbB0l6L1sNDafU0z95spsTq6rvPGG/83gcBIyu12u4PR0eG0on8rCgRG6ej4gEuX\nzjE5GU85VuDzzbTctNvtizzD1pSVRb8M58qOmJKP+WJKPuaLKflkLmZPTz/B4AS7dpXj8XgoKiqi\np6eTUMjBoUN5i+7tm37eUAg0zcvcAWjxeHLQkbFA59H1mbeTPnBteHjxN1TZ9jPJlnw2NqZKYWEd\nd+8mi/6iojIOHWrB7z+I17v869IMx5Y7vtohWpBc6tR/oYd37pyjuze95WZlRTWtBw/zSNODlpuR\nSGrbMLOdBBnOtTIynCt7Yko+5osp+ZgvZq7nM70B0QznYHpJzsjIALdvO7Dbi+no6OVXfmUvhYVF\n1NZ6GRqaoqLCuWw+igJuN+TPGSLr8yX3PCy2BGq5XBdaMuTzLb9cKBteB7mWz8M+r2EYTExEcLvz\n04498cRxIERTUwtVVTtQFGXZGVEPm896H1vq+EqHaEGy5ebFa9doO3uW+3194HSS/2DvgkVVObh3\nLy1NTWwrLX24QVpmfJGs5ZgM5xJCCLHZYrEYb711g2AwRkGBSkvLTmBlRexGmV6S09sL8biXqakw\ne/dOoWkaFosFq9WKw2Fd8fPNXV8/+/3a19gv1Id//tTj9TA8PMLIiE5hYfr0Y7GxJifjtLd3cO7c\nGfLzvXzuc19Ou09hYRGf/eznNz85ExmPRPiwo4OPLlxIttycI9/t5tjhwzQfOpRsuSlWRIp+IYQQ\nG+LatX4mJsq5cmWcWCzK+PgUicTEhhSxq+FweKmr286tW3cJhyNUV09hs9mWfIymaXR39+N02nG5\ntgELra9fnw3LK516vFZXrtyis9PF+LhKNNrFkSO7NiyWmBUMBujo+IAPPmjHYok/uG2UwcF7lJZu\ny3B25jEwNETbmTNcunYtreVmeXExLY8/zv7du7d0dy3DMJicnFz1noWte8aEEEJsKJ/PydRUGJst\neWXf6y0mEll6WtRCy1t03U9yLfv6ycuzcuRIDePjIxw4sHxHj/ff7yQc3oGuRykr6+X48ZoFe+Nn\nw4blwcEELlcpug5DQ4FVPXb65xMKzTZCyeQnN9lC13X+/u+/x/h4mMnJ2cm5lZU1aJqW2eRMQNd1\nrt+8SVt7O703bsyeIJLdifbU1XG8qYkalwulaOsOHUskEly4fJkzN29SUlTEv/3VX13V46XoF0II\nsSF27txOMHiR/v4JysvrUJTlC8OFlre0tkJx8UZd+V5ZC7+xMQs2mxNwEgyOLHt/Mysvt3Plyh0i\nEQv19ekbJpcy/fPRNC9u98YtP8o1qqpy6FAzv/zl26iqhX37DtDU1EJZWXmmU8uoWCzGuUuXOHPu\nHMFw6lI5u81G08GDPHLkCAXTw0G2yOTc+cJjY3zY0cHZixeZGB0Fp5Oh0VGCoRB+38q7OUnRL4QQ\nYsPs2LGdioowmhZlfDy6ojXvG728JRYLM31xdaVr8Kuq8rh58zYWyyQNDRvbInOj+/Dv3l1NeXmI\nkRGDnTtXv6wk+YasMGUDs0iamIgQDoew2SrSjh0+fBRd16mpOUZl5dY+eYFQgA86znHu0iUmE6ld\nfAp9Po5/7GMc2b9fWm6S3Mj83b/7O6KxWMrt5aWlRGMxKfqFEEKYw/S692BwepJvZod0rTWfQ4dq\nqa+fwGLJY3x86fX/65HfrI05X16vj6mpdX/aLWtw8B7t7W1cvXqRgoJCfvVX/0/mf4rkcrn52Mee\n3qoXqzEMg76+W7S3t9F7+SxOR2rLzZ1VVbQ0NVHv96MWF2coS/PJy8vj0N69nDl3DkVR2FdXR8sT\nT1BZUbHqoWxS9AshhNgw0+veDWPxNpbzbeSV7tXmo2kaFy70MD6uceDANgoKXOuWy1L5CfMzDIOu\nruucPdtGf/+tmdtHRobo7++mqKguc8mZyNTUFNeuXeTs2TaGh+8DYHnQYz/PYuHQvn0cb2ykrORB\nJ6kt+q4oGo0SiUYpXqAd5/HGRvLy8jh2+DB+TVu+TegisrLol+Fc2RFT8jFfTMnHfDEln9Rjuu6n\ntXXuUS/RqD9jA4muXOnlzp1KrFYH//zPV3jmGZ/Z5u1saj6hUHLqcDyenEkQi4UJBr0pw8hy8XW5\n2PG33jpFIDA0c5vVamPv3iYSiaJNfc1u1POuZYjWtMitEFfOnOfC5Q6isWjKMbdu8PTBQzTv24fb\n9eCN9PQJM9tJ2OB8hgMBzly8SMe1a1SUlPDvn3su7XGFwDMHDoCmyXCu5W5fyfGtMK8hEzElH/PF\nlHzWFnNycpJEQqGwcPEe7rlyDjKbj5q2aTeT83acTg2fz4qqQjxumbk9E+d9KZuVj9/v54UX0pdH\nzW/ek3uvy4UoPPbYcd588//D5yugsfE4+/cfweFwZOQ1uxHPu5ohWtPu3rtHW3s7lzs60Ox28hTw\nPGjGU15aSmtzM/tLSrCULDEjwkwnYQPyMQyD7r4+2v71X7nR05O80WqlNxjk7uQkFaWlMpxLCCEy\n4YKFLUoAACAASURBVObNfi5enGRiQueJJzxUVpZlOqV1sVntMbNRIpFgdDRAfX0podB14nELhw6t\n74babLSW5VrZzDAM+vt7mZiIsHv3/rTj+/Ydxu3Op7a2YUu3LdV1nWtdXbS1t3P7zp3pG4Fky829\nu3bR0txM1fQ69C26hGearuv8w1tvMTZnDkGexcLhfftwOhwz5269SdEvhBDLuHlzAperAV2Hrq7O\nnCn6N789ZnaYmpriX/6lk8nJShRlgGee2YnL5Vz+gSJnJNehX6Krq42hoXu43fnU1aUPhLJareza\ntSdDWWZeLBaj/eJFPujoSGu56bDZaDp6lEeOHFlVh5mtwGKxcGz/ft66eBFvfj7Hjhyh+dAhXNPz\nCTboTZEU/UIIsQyPR2d0NMbkpIbfn1tX8za6PWY2ikTGiMfLcLt9xONW7t8fZefO7ZlOS2wCXdc5\nffoXXLjwESMjkZkZUZHIOJ2dV9i371BmEzSJkdFRzrzzDh23b6e13CwqKOB4YyNHKiqwleXGBZK1\n6r93j3g4TN2OHWnHju7fT2F1NXvr67FYVjcvY62k6BdCiGU88kg9nZ23iUQsHD68dEeOq1d7uX17\nEq9X4/jxrf2Rv9kttLzJ5/Pjdnuw268zMWFFUQbYtq0mQxmKzaaq6sxynmklJdtobm6loWFfBjPL\nPMMw6Ll9m7b2djq7uyEaTZmcW1tdnWy5WVu7pZfwaJrG1Rs3aGtvp7+7m+Lt2/m9L385rb2my+nk\nwPbNvZggRb8QQizDYrGwd+9ORkdhqbbI0WiUa9fA7a5nZGSCnp5+6uqqNy9RsSoLLW86eRLy8gp5\n5pkGRkcD+Hw7cDgcGc5UbKbm5hbu3OmltnYPTzzRwvbt1avuh55LEokEF65e5Ux7O4MjqdOoF2y5\nuUVpmsbps2f54Nw5wuPjM7cPj47S1dNDfW1tBrNLkqJfCCHWiaqqqGpy4pGuT6at/zWjjZ7+uh50\nXScQCKLrOmNjoZnbPR4fhlHIw2w8nl7eFA6HuX79Pn7/TXbvPoyiqJSUFMsnNTkoFotx6dI5pqYS\nNDQ8kXa8traB3/qtr6LrBQ/VUSnbjY2FOdP2LjduXWQimtpy0+N2c+zgQZofe2y25eYWp6oqF65c\nSSn4i/1+Wh5/nJrKygxmNsv8/yIJIUSWsNvtNDW56em5QUVFHjU1OzOd0pIWmv6qaZmblruYsbEg\np0+H0TSN8+ejWK0epqai7N59j098Ir3FJ8DAwBBnzwbYtk2hqalu2eL9woU73LjhIxbzcuHCENXV\nTk6eRAZl5ZBAYJRz585w6dI5EolJ8vKsVFcfA1I3aauqit9fsFVXpzAwcIf29jY6Oy+jRELM3cNe\nUVZGa3Mz+xoasIRCIAX/DEVRaGlu5qf//M/s2rGDlqYm6rxelKKiTKc2IyuLfhnOlR0xJR/zxZR8\nNj6m272NAwe2Adkwa0YlOfZl1vAwaT3XNy+fhQ0NgaYlP4GwWgtwOAqJxcbQtFGGhpJDoubSdZ23\n3x4lFtvNxESMqaleGhpS34ANDoLdDpFIcq1yNKqi6w4UxcXkZAJNcxAMkjJ0aiP/nNkynCtTMR8m\nn/v3Df73//4BPT3XMFJ+oAk6OrpwOA6ue0yznYPlhmzpusKN7k7aL3zE3Xt3Zg5ZIhEUxcK+2lpa\nDh+msqwsudQpFDLfCdqEmJOJBOevX0dRFI7u35/2uIOlpVT92q9RMv0R0eDg4mtCM/DLIiuLfhnO\nlT0xJR/zxZR8zBdT8ln6WEkJ9Pcnv3Y6YXqJvdudPDb/sZpm4HarOBzgcKi43fqCz68oYLGEURSo\nrIzT1zeEx+Ojvn4PeXlj+P2L96LfqsO5Mhlz7fkoeDwqDkey4LdY8ti79yBNTS1YLGVb4hwsNmQr\nGo3Sc+eX/Ms7NwmNjQGzg7QcdjvNR5o49vjji7fcNNsJ2qDnDeXl8UFHB2cvXCAWj+N2uTjS2pos\nouc8zgqUzO9YtNnnQIZzCSGEyGaxWHJ5TzgcJRodY2oqSiw2BXjS7muxWDh82M25c534fDp79+5a\n8DnnLm8KhdwUFropKdnJxISs489WhmEsuOm2ubmVO3d6OXz4GIcPH8XlcgNbtsEMw6OjnGlvp+Py\nZRJjYyldeIoLCzne2Mj/z96bh8d1Vfna76lSSVWSalBpsKx5tCVrLtmSbGyHECfYkEAICRBomhDo\n8HE7NA19cwlcIN1c+AgBmr400NA0U6fD1JCBQCAzcQbkqTR41GRZsiZrqkEq1Vzn/lGaSqVZtlWS\n9vs8eWKdffZe65ya1t5n7d+q2LWL6PHx8EdpWwi/38/jzz7L+YEBArMKZjkmJmi/dImiDbbpQwT9\nAoFAIIhotNpgcB4IBDhwAMAHqNBqk5Dl+fcg5OSkodOFLoidPNlKb69EfLyHsrKdKBRRITn7SqWd\niQkrDkfw35G4qVkwP4ODfRw7doxAIMDb3/7usPa0tAz+5m8+dd300CMRWZa52NVFvdlMW2dnWHt+\ndjZ1JhMFublbWq1oNkqlkgmXazrgVygUlOzYQW1VFRlpaRtu1iiCfoFAIBBENAqFYjo4T0xMCmlb\n7m+uwzHO5ctxxMWl43Q66e7uIyVlRk519qq/1QoGgw69PvI2NQtmCAQCtLdfwGyup729G40muJly\n//6b5n3ttmrA7/V6OXe2iZaOBobmkdys2LWL2oMHSUlKWmCErU1deTkD9fXsLi9nT2UlOm3408WN\nggj6BQKBQLDpUamikaRgwSW/3058fLhiy9TEQpYXzuW3WKz097swGFKEnOc6Issyjz76A4aHr4Qc\nj46OYWjoipiwEZTcbGg4zunTp/BaBkNUeHTx8eyprKS6vJxYp3NtG0o2OIPDwxw7epRoo5G3vvnN\nYe2F2dl8urwclUp1/Z27yoigXyAQCASbnujoaA4cSKajo43Cwlj0+vBKmHb7GG+8cRmrVcG+fQay\nslJD2i9fHuDkSR9Op46RkQscPLi1K7SuJ5IkkZmZMx306/WJ7N9fS0lJJdHR0evs3frS19eD2VxP\nW9u56bSUqWcc6amp7K2upriwcObJxxwN/q2ALMu0d3ZSbzbT0dUFTicqrZaDtbVoNOELAopNEPCD\nCPoFAoFAsEVITEwgMTG4hD9fWtCZM30oFLuIjoazZ1vCgv6+vjHi4gqRZbBaN0cQEOnIsozL5QTC\n9eCrqmoZGRnCZKrDYCgkMXHr5qH7/X7a2s7T0VFPf39PSJtCoaAofwe33FAdzEPf4vj9fn7w6KNh\n1YUB+q5cIT8n5/o7dZ0QQb9AIBAIBIBGo8RicSPL0ahU/rD2rCwD9fUXcTi0FBSEtwuuHl6vlwsX\nTnPqVD0ajYabb/5w2DkJCUbuuuuvgQ23n/Kq4XROcPq0mYaG4wwN2WeL8KBWaygrq6aycg/JPse8\nkp1bEaVSSUpS0nTQr9dqqamsxLRvX9gq/2ZjQwb9ojjXxrAp/Ik8m8KfyLMp/IkcmxkZuYyNdeJw\nBCguzg37rYmJSaamJo7eXhf5+TtX/FskinMt3eZwjHHmzAnOnj2JyzUx3ZaR0U9x8fbr7s9iXItx\nlyqiNZWoMzI6jPn0Kc61nMXnmzzfIQMSRkMipopqineUEK2KBp9v0ugCId9GukErsCnLMl6fj+ip\n1JxZbXV5edgGBthbUUFRbi6K4eFgmtN8qU4b7R4swoYM+kVxro1jU/gTeTaFP5FnU/hz9WwGAgFs\nNisQrLgrSUFlnvk23YaPqSQ5uYDR0cXsxaLRxIriXNdgXFmWefzxH2G3W5GkGen4bdvSMBi8G/p9\nudy2hYpoAWyXR+iw2ak3m2m/dAkAjYpgRSigKjOJuoMHyc/JmZTclAH3ZO+ozXGDlmHTr9dztqWF\nerMZvVbLe9/5zrC+GUYjH9k1a0+OQrF57oEoziUQCASCrYDNZuWPf7SjVuum9faPHCFEj18QmUiS\nRFmZiddffwlJkigoKKa6uo60tEwslq2br+/xeGg6d45jr73GsNsd0qaKigpKbppMJEvSllbhcUxM\ncOrkSU5cvMiYI6jU1T84iMVqJcEg1JxABP0CgUAguAbMXnEHsNkWX3W/mqjVOuLjt27wE+k4nU7s\ndivbtoWn61RU7MbjcVNRsWfLy27a7HaONzZyqrkZl9sdTD2ZfPyhi4+n1mTCVFo6k4e+VTc2ENyc\n+72f/QzHyEhIdeFkoxHHxIQI+icRQb9AIBBsUeYG5sFjBmDtQfnsFXdArLoLGB0d5ujRY3R1NaLT\n6bnnnr8Nq/yq0cRy8ODN6+Th+iPLMpd7+6g3mznf3j4tuTlFZloadSYTRQUFW7bY2HwolUrKioqo\nf/11AHbk5VFnMpGblSWqC89CBP0CgUCwRZkbmLtcdvbuhaSk8KB8aoIwtWIPS6/aX+sV9ymfZv+m\n22wWZFl/zWwKVoYsy3R1XcRsrqezs216sXp0dJhLlzrIzS1YbxcjAr/fT2vrOZpf/RO2sYGQNoVC\nQcmOHdTm5pIxOw99C+L2eBgfHSVxnjSmmspKAjYbtQcPztsuEEG/QCAQbGmWG5hPTRD8fh1xccEJ\nwnqv2ttsVuQXHicmcUbD3TM6hIWbkCTF9NMF0K2bj9eDnp5+OjrGyc9PxmiMvDSGP//5WUZGZhRH\nlMoodu2qQK9foOzxFsLpnKC5+RSNjScYH7ejdI5NV87VqNXsLi9nT2UlOq12S6fvWG02jjU00HD8\nOClZWdz7vveFnWNMSOBtBw9u6X0NSyGCfoFAIBAsi+ATASPx8evtyQxadSwJ8drpvwOBAId2a9Dr\nwWoFg0G3qXPDBwaGaG6WSEkp5NVXWzhyJDaiKtJKkkR1dR3PPfc74uK0lJfXsH9/NRpNeLGtrcTw\n8CBm8zHOnWvC7/eFtCUnJlJnMlFeXIxqk1SCXQ2yLNPd20v9qVNc6OhAlmXweOju7aW3v5/07QtL\nuArmRwT9AoFAILgmuFz26X9fr1X3K1dGOf6XYRISXJSV5ZGQsLmL7djtE6hU2wCQ5Xi8Xs+6BP19\nfT04HGMUFhaHtRUVlaFSRVNYWIzNpmST1z9aEFmW6exs59VX6xke7ghrz80tpC4/l+rybJGHTnAC\n/9s//AH7+Pj0MaVCQUlxMeqYmHX0bOOyIYN+UZxrY9gU/kSeTeFP5NlcT39sNhgZsTOpbofLZWdo\nSId+npR4my0YuHun6gA5givpsjy/zUDAwN69M38PDUFysg6/3xD2Hb7ae2Czgd8Ghrjg37Isc7HT\ni09XiMORQH19OzfeWLiiMdfiz7VqWwy9Pg2nsxW/P4Zt2wK43enMVnW8lu9Lv9/PxYvnaW6u58qV\nHjSaOD74wUJGR+eGFipSUkqx2Tbm52QhlltIy+P1cK7lDOamU1hsozgdMpq4YFAfFaWipKgUU1k1\nxoREYgYvI1ksq3Nok315KYGa/HxeqK8nVq1md0kJe7ZtQ5uTEzzvelXH22hvzEXYkEG/KM61cWwK\nfyLPpvAn8myulz8Gg4E775x9NBiUz9dPkqZW6oMolXYMBh0Jc9KyZ/oqQjYE6/Xz+yPLMh0d3bjd\nXoqLsxZcpV7IJ0vMxEzYJYGkchAbKxMT4yA5WRWxr4ndbsfj8ZCYmDi9qru6VGQV73hHCQkJ8oKr\nw9eiiFZDwxu88soxxseD74ng6r2DwcEzpKVVbqrPyUIsVkgrjVFsUVGhkpuAVgNanOhTUqipqgqV\n3MTNmopoLdUeoS/KwOAgE04nednZYW2mN72J2G3bKC8uJioqiiUq563N383yxhTFuQQCgWBrMJ/K\nDsyvtKNQKMI24i60V1CvN3DkyFSePEB4rrwsy5w61Y7VKpOTE0t+fsaS/p4508nFi9vQ69WMjLTw\nlrcsX51ErzdgPXQH7lluFNWMc/nyEHp9DNu25Sx7rOtJV1c/ZrMPSYpl+/ZWamt3rnnM65kOIkkS\n3d3t0wE/gNGYhMlUx44du5iVjbHlkGWZy3191L/yCheuXJlfcjM/n+Ldu695vYpIJhAI0NbeTr3Z\nTOflyyQmJHD/hz8c9j6O1WgwlZWtk5ebDxH0CwQCwSZirsoOXB2lnakJgiwzvbofCASwWGZmCWfP\n9mKxZKPR6Ghu7iQ93YVarV503LExHypVHAoFuFwr+0lSKBTo9caQpw0JCUYyJucakSp20tc3Tlxc\nMO1oeDhCnVyC8vI6/vznTnJyCjCZ6sjJyd/Seeh+v5+zLS3Um830XbkSUkhLoVBQunMntVVVwc2n\no6OwRQP+QCDAicZGjr3+OqMez/TxEYuFtosX2ZGfv47ebX5E0C8QCASbjOulsjNX57+11UtOjhWN\nRsdy47+dO5Pp7j6Py6WiuHhlOzxPnWrj0iXIz5eorNw4eu/bt8cxMHB5cqU/sHSHdcDr9dDU1Izb\n7aKmZn9Ye07ODj784fsxGpPWwbvIYcLp5GRTEycaGxmb2hwzSaxGMy25qY0kyat1RJIkzKdPM2qz\nTU+KDDodtVVVZKWnr7N3mx8R9AsEAsEK8fl8DAwModfHo9Vql+6wjshygImJMdzuayP9N1vnPy1N\nJjX1IrLsprw8dslVfoDExARuvtmAwRBYUYXRwcEhLl82IkmJdHYOkpU1inEZSfEej4dz57pxuZTU\n1GSvS4pFTk4aRqMdl8tNcvKO625/Mex2G42NJzh27BQKhZOoKBVlZdWz8s6DSJK0pQP+4eFBml95\njkuXz+Hz+0PaUhITqSsooKy2dktLbs6HJEnUVVfz1OXL5GRkUFddzY68vC2d6nQ9EUG/QCAQrABZ\nlnn55Qs4nTn4/UMcOOCdt4LteuJy2ZmKQy5c6MFikVAoXBw5EkdCwtqq1QZTeqxAsPrtxEQUkqQi\nNjYOSZKoqMhfcRqRJEkrCvgBoqKikGXX5F9eVKqlJxgAr7/ejtu9E5vNg0bTSUXF9UknCAQCBNO7\ng8GNTqdDF0E1w2RZ5o9/fIKWljMEAgHc7uBCrM/npaOjhdLSyvV2cd2RZZmLF9swm+vp7r4YUkgL\nYEdeHnUmE7lZWUEFni0a8Pt8Ps5cuEDAasW0P/wpUVlREal33cX2nWvfyyJYGSLoFwgEghXg8XiY\nmNCh0cQD8fT0tEdU0D97w21cnAeXS6aoKIPYWAOXLnWsOegfG7Pyl78EU3ocDjh3DpTKCaqrF+4z\ntbl4bsrPfJuLl4vRmEBJSTdtbW0UFWnQapcXQbvdUSgUSlQqDWNjvqU7zGFiwsnZs73k5mrJyNi2\nrD5dXf00No7hcAS48cZEtm9PXrHda40kSciyPL3xVJIUFBWVUFVVS1ra0huyNzMej4ezZxtpaDiG\nxTIS0hatUlFZUkJtVRWJW7wS7LjDwcmmJk42NzPucBAHlNfVBVV3ZhEVFcX25Mj7DGwFRNAvEAgE\nKyAmJgaDYYyRkWEUilGys5cX+F0vZm+4NRohOXkYpzOaiYlu0tOvTlAyO6VHqbTj97twOFy4XDLz\nFd+y2ay88IKdxMSZtquxuXjHjiySkmbU6+TJogGLbSgtLIzj9OlWPB4/xcWpK7IXCAR4+eWLOJ3F\njI4OAQNkZCw9RmvrGBrNDvx+aGlpXfegX5bnl/esqqrl0qV2ysqqycmpISsrgh5FrAN2u5XTp49z\n+rQZt9sV0qbTGaipquLGfWXLSmPbzAQCAX733HOcPn8e/yy1IofTSUtHByViRT9i2JBBvyjOtTFs\nCn8iz6bw5+rYLCkpZmzMilqdhSyrGR2N3HtQUVHElSuDaLVGoqJ0a/IVgkW2pvYrBgIGdu6EiQkL\nZWXgdifMW3zLZgO3O7i5eAq/f6a41+BgMHAYG7OG2dNqDQwPL/w0YMrfy5f7OXPGjlIZoLp6G4mJ\nxnmvJSEhnf37AwwNSUiStKLfE6/Xh9Uah9utQKdLprPzIrGxS/f1+QKMj3uxWPwYDCuzuRyW+3qO\njg7R3FyPz+fl0KE7wvqp1RnceeenUalUDA4urn60kb4rlltEC4ITor6BXk41neT0mRbUc17fjO2Z\nVJVXU5BbiGa4F/XEBExMrMyhSP2yWGWbArAPDOCf/GKQJImdOTnUpaeTnZR0/YpoXatxN5I/S7Ah\ng35RnGvj2BT+RJ5N4c/VsKmYN6UnMu9BFCkpaQv2c7vdvPJKI34/lJSkEhOjRZIWTr1JTob29qA+\nu8NhZ2LCQ3y8gqwsI7JsXLCIll5PmJqQwTAj/ylJM2lDU0w9DUhJmX/c2ddy4sQYycnBFcW+vhYK\nC40h1xmKAoVi6Xvn9Xp5/fU2JiaiyM2Nobg4m8JCN62tXajV41RX54YF/fPZvPnmAs6d68LhUFBb\nm7+gWuNaskMWK6RltbZjNtfT1dUBBIMyheJGUlIS5uk3k4ceaZ/N1fqzVBEtjMZwyU0gOTYou6lU\nKCgtKqK2qoq01KknO15QrKGQVmR+Way6re7AAXqfeYaq0lJqKisxJiQsXUhrk92DiLF5tYpzvfji\nizzwwAOYzebpYx6Ph+9+97s8/fTT2O12SkpKePDBBykuLg455xvf+AbPPPMMExMT7N+/n89//vOk\npKSsxLxAIBAIrjKvvdbEyy8nodEkcOJEMFddqVw49UarDe4Z6O29QlNTgMTEBFSqTvT6PKzhC/Ur\nYnbaEIDL5eSVV1qABG64Yfui+xE0Gj8Oh49AwEdS0so2BS9Ea+tlJiYKiYpS0dLSSX6+h5qanWRl\nuUlJUS17P0JUVBTl5fnXXZ5dlmV++9v/YGysN+S4UhnF4GA/SUkJC/TcOjgmJjjZ0sKJpibG50pu\nqtXsqatjd0XFlpfcHLVYON7YiEKh4JYbbghrL8zL49P33UdMTMw6eCdYLssO+s1mMw888EDY8Uce\neYTf/va3PPDAA2RlZfHjH/+YD33oQzz99NNs2xbMdX3ooYd46aWX+OxnP4tGo+Gf//mfue+++3j8\n8ceFTJNAIBCsAq/XS1PTJeLjZSoqcoiOjl7VOD6fjFptQK02EghMEBe3+KrV1J6Bjo4RkpODBaac\nTud0Pv1CuFz2kEqtLped+fL/Z9PZOUpx8U7AiNl8gZtuWjjor6vL59y5S0RFKdi1K2/RcZeLWq3C\n7w/KVioUnmmFoejomA1RW0mSJFJTM6eDfq1WT1VVDaWlJjQaTcQWL7seXBkaot5s5rTZjG/OZ2db\nUhJ11dWUpaQQtYUXJ2VZpqu3l/pXX6WlowNZllFFRbG/pobYeSRcRcAf+SwZ9Hs8Hn72s5/x7W9/\nm9jYWLzembw4WZZ5/PHHuffee3n/+98PQFVVFXv37uUPf/gD9957L93d3Tz11FN885vf5MiRIwAU\nFRVx+PBhXnzxRW6++eZrdGkCgUCweTl58iLDw/m43RL19W0cPFi0qnHKyjI4caIPcJKXt/yNm/n5\nKfT0tCDLOrZv9y4quanXGzh0KJjOM4MOvT7kwOREYAa/f5xAIIAkyUjS4pOK6Ojoq16gKz8/E5er\nE5ttgKqq5BXLil4vZFnG5XLPu6G0rKyGiYk+qqpqKSws3tILbbIs09rRQb3ZTOfly8GDk9q2kiRN\nS27mZGYGNzpv4VlRIBDgR7/4Bb2dndNFtKaO9/T1icq5G5Qlg/6jR4/ywx/+kM985jNYLBZ+/OMf\nT7f5fD7cbjdxU7XeAY1Gg0qlwmazAVBfXw/AjTfeOH1OdnY2BQUFvPrqqyLoFwgEglXgdgfTNBQK\n8HiWWf52HjQaDXl5WtTqWMCHwzGKUrn0KnxCgp63vU2Dx+MmPn77oucqFAr0euN0/v58TEmNzmbf\nvmza24ew2Yaprc1a3gVdZUpKctfF7nLw+/20tJzltdeOodUquPvuj4Sdo9cbed/77l0H7yIHt8fN\nMfNZjjU0MDonBy1apaKqqoraqqpgHroACH5mExMSgkE/EB8Xx56KCqrLy4mfFfMJNhZLBv1lZWW8\n9NJLxMfH86//+q8hbSqVisOHD/Nf//Vf7Nmzh6ysLL7//e/j8Xh461vfCkBnZyfJyclhKxCZmZl0\nTr6ZBAKBQLAyyspSePHF8/h8ElVViaseZ26wbbWCwRC+Cj9FR0c3Fy64yMszkpqatOq0orlMpQ3N\nJTU1jdFRuNpxxsTEBCdPXsLnU1JSoiM7e/GJSyThdE7Q1HSSxsYTOBxjOJ0wNgb9/b1s356+3u5F\nDFarhcbG45w//meUSk9Im0Gno7aqiqr0dNTbN85rfy3w+XzzBoN1JhOD3d3sPXCAkp07w/T2BRuP\nJV/Bqbz8hfinf/on7rnnHu666y4g+MX98MMPs2vXLgAcDgex88gbxMbGMjAwsBqfBQKBYMuTmJjA\noUPzqa+sjLnBtiyz4Ip8b+8Ara1xJCdncfx4O4cP665a0H+9OX++B1kuJipK4syZllUF/VNFx2Zj\ns4HBsPqiY0shyzI///l/YLWGpp4kJqbg9XoW6LUw811DcMK3MdOAZFmmp6cLs/kYHR0XkGUZpccz\nXTk3OyODOpOJnfn5wddoi6bwBAIBWiZTnTQ+H+/7wAfCzklLTeVjd92FlLj6RQVBZLGmaZvP5+O+\n++5jdHSURx55hG3btvHss8/yuc99jri4OG666aYFi4AAWzq3UCAQCDYaTqcHpTK4mdbvj8Hv9wEb\nM+hXq5XY7W6iomKIivKvagybzcof/xgqMzoyYufOO9dWdGwxJEmitLSK1157EYC8vB3k5dVRXp67\naFGyhZh7DVMyqbNrKmwEfD4fZ86cwWyuZ2godEFRqVBQWVJMbVUV25dYyNzsuFwuGs6c4VhDA1Z7\ncA+N5HJhsVpJMIQ/3VvNe0oQuawp6H/hhRcwm8385je/obS0FIDa2lqsVitf/vKXuemmm4iPj8cx\nRwYLgk8AtFrtquyK4lwbw6bwJ/JsCn8iz+ZG8kevT8fvb8ViiSItTYHTGYvTeW1tXqtxjcYcZLkT\np9PPrl3ZIb8ry/XHZgO/P7TomNs9U3RsLb56vR5sNgtJSeFBakZGNQUF45SV1WAwJDI4CBbLOfhg\nlQAAIABJREFU8sad2xYTE3oNU0XT3O6F+630WpbbtpIiWlM4JsZpOttI/bEGZEVokaxYTSwVpVXs\nSUkmO3tyYjY3gIi0N+Y19CcQCPC9Rx/FPicmS1AosPf0kDCrmu718Oeqt62HzUjzZwnWFPR3dXWh\nVCqnA/4pTCYTzzzzDE6nk5ycHIaHh/F4PCGPgXt6etizZ8+q7IriXBvHpvAn8mwKfyLP5vXwZ2ho\nhLEx52Qai3KVYyo5cqR4VX0tFivt7UMkJmrIz89AlmUmJhzExKiBqHV4TZSUlCys9rMcfyQpuNdg\ntoS7Xh9adGylvtpsVhobj3P6tJnY2Dg+/OH751ltjSU9/UhY39Vcy3zXYDAEJy3X+zVZThGtKQYG\nB4OSm+fP4w8EiFc4p1VmUpOTqTOZKC0qCuahX6siUWvpuw7+KIxGSqqq+MupUwDkZmZSZzJRaDCg\nSEq67v6I1+QatV2t4lxzyczMxO/309TUREVFxfTxpqYmEhMT0Wg07N27F7/fz4svvjgt2Xnp0iXa\n29v5u7/7u7WYFwgEAsEyuHSpj8ZGCaUyga6uC1RUlIS0z87rttmCgSAsXJV3LmNj9sngPTTVZ2pc\nn8/LSy91kpi4g74+H9HR/XR2jjI6akSl6qaqKhfQzDv2WpkvZz14/Orkrc+VGV1O/YG5yLJMf/9l\nXnutnvb289M1D9xuFxcvtpGfv2PNfi7G7GtYjf/Xi0AgQOvFi9SfOsWlnp6QNkmS2FlQQJ3JRHZG\nxpZOS/F6vYw7HPOm69RUVuJyu6kzmdiWnBw8uEX3NWxF1hT0Hzp0iMLCQv7+7/+eT37yk6SkpPDS\nSy/x9NNP84UvfAGArKwsDh8+zBe+8AXGx8fRarX88z//M0VFRRw6dOiqXIRAIBBEEnMDzalAerlB\n9NVmcNBBbGywkNbYWHgO/uy8bocjuPI7ldu9VG76G2+c58oVAwpFP2VlqRiNMwW0psZVKmM4f95I\nbKydigo1w8OjjI4mEBu7HZ8vie7uHtLSro005nx59y6Xnb17ISlpbXnr88mMWq0LKx8txtGjf2Bi\n4sr03wqFgp07S1c11koIv4ag/2utrnw1cbvdNDQ1cayjA8ukHPgUMdHRVJWWUpOTgzE3cuVVrwdj\nDgcnzp3jZHMziQkJfOTuu8POSTAYeOekuqJg67GioF+SpJDZc3R0NI899hjf+ta3+Pa3v43FYqGg\noIBvf/vb3HLLLdPnffWrX+WrX/0q3/jGNwgEAuzbt4/Pf/7zW3omLhAINi9zA02HA5TK5QXR14Kc\nHCNvvNGOLMeTnj5P3i6gVuuIjw/6NjvVYzF8Ph9DQ2ri4rYD2+nubiMvL7Rq7tS4GRkyo6MWoqP7\nKC420dvbgc+XhNvdT3LytQ1sZ1/b1WQ+mVFZZsXVeiVJory8jvr6p9BoYikv301l5R7i41e3720l\nLCSVGgmMWiwcb2yk4cwZ3DZbSJGoBL0+KLlZWhqsBLuFV6v7BgaoN5s509BAYFIefcLppKevj4y0\ntHX2ThBJrCjov//++7n//vtDjul0Oh566KFF+2k0Gr70pS/xpS99aeUeCgQCwQbkWgWaqyElJZHD\nh+Pwej1otalXLT6KiopCo3Hg8Tjx+UZJT184LSQnJ42cHDUHD25HrVZz6FA+ly71kZysR5KWVxRp\nvlSd5aTp+P1eAgE/CsX6VtQdHr7C6KiFgoLw6smFhWXo9RI7d5agUqnWwbvIQJZlunp6qDebaeno\nmE51miInI4O66mp25OUJBUCCn4lf/e532MbGpnePS5JEcUHBhpXTFVw7RKUFgUAguIq0tV2mqamf\n1lYNVVU6lMrI+JpVq9VhRRKvBjfeuJPu7n70+ngUivBJzlS++NTTjql8cbVaTVFRDrD8RVqbzYrn\nj4+jVQdrv4y5JpjYe8eiaTptbT00NqrQaLzs3KlDr7+++eqyLNPR0YrZXE9raydGYyw5OQVhhY6i\noqIoLa28rr5FEj6fj5bzZ7jQfoorw8MhbUqFgrKdO6m74QZSU1LWycPIRKFQUFNVxfNHj6KOjsa0\nezc1lZUY9PqlOwu2HJHxayQQCASbAJ/Px7lzbhSKAtxuHz09Qxum0uvs4Hzm76UDZJVKRX5+FhAe\nvM/OF1+q0u9y0apjSZiV9jKxyLkAXV0uJElHIBBLR0c3O3akXZfNqrIs09BwnIaGYyGFtJzOCS5c\nOE1padU1tb9RcDjGaWw8QXPzSdwjA9NFtADi4+LYU1FBdXk58W730iopm5iR0VHGenvJmecemEpL\nUUVFUZmWRvQWr0MgWJwNGfQLnf6NYVP4E3k2hT/X1qbfL+Fw+PD7wW63YLPBlSsx2GwQE2PHatWF\n6bdHwj0IBAzs3Rv899AQBEU9dPj9hjXKmiuY0n93u4PZB/NtEF2JLn68A6aSXxyOoL8LLWoGdejj\nKCjQEAiMkZ7uZccOAB1OZ/i1rdSfxdskmptb6O+fMSJJBqqqaklMLF7RfV2K9ficrEZTf3bblaEB\nzM2nuNB2nkAgWBxN6XAAClKTkthbUUFJfn7wiYjbHRkflOtsU5ZlOnt6qG9uprWrC6NCwSfS0sL2\nQ2qAmuzsYN+FUsM24f2JCJuR5s8SbMigX+j0bxybwp/Isyn8uZY2lRw6lMyFC4Pk5jrYtSsHSQpd\n5Z4vDXn974FiOkVGr48Ef2aYyuOXJJAkC2PuIZDGidXEElC6SE5efNyCgp1cuNCFRhNNXl7ldMC0\nFun25V7LgQN1PPnkRTIysjGZ6khI2ElS0sJ56GtZyL7en5OVaOpPEQgEsF0082xTB12TkptxMcE2\nSZIoyiug7uBBstLT5xf6iKQ35jUcN2Aw0Hj2LPWnTjE4MhI8qNEw6nTSarGws2Dh+hLiC3wdbEaa\nP9dKp18gEAgEoaSkJJKSkhhyTJYXLta0FjweN01Nl4mPjyE/P/PqG4gAbDYrL7xgJzFRRyCgxxlz\nK27XGIf2a9HrE1D6F08XCubK518T33w+Hy0tZ5mYGGfPnjeFteflFfLBD/5/pKSkAltXYMblctFw\n5gzHGhqwXrkSosITEx2NqayMmsrKYEXYLZzCM4UkSRxvaJgJ+AFtXBw15eVkpqevo2eCjY4I+gUC\ngWCD8sYb7cTGFuH1juP3d7NjR9Z6u3RNiI4OaogqFAri4oKzJ61WT0KCcV0C6YmJcS5cOElz80kc\njnGUyihKS6vQaGJDzpMkaTrg34qMWiwca2ig4cwZPN7QVCCjwUBtVRWVJSVByU3YurOiOUiSRJ3J\nxJPPPkvatm3sra5m144dKOfIlgoEK0UE/QKBQLBBcblUxMcriYnRY7FcWbrDBsXlstHWpiQ6Orj5\n1m53cuAAJCYmXVc/ZFnm+ed/z8mTjcTE+KeP+/0+WlvPUVGx+7r6E4nIssylnh7qjx6l9eLFMMnN\n3PR06g4coHCLS24GAgHOt7Xh9ngwlZWFtZcWFZGYkEDGPDn8AsFqEUG/QCAQbFCys1UMD3egULgp\nKspYb3euKdHROtTqYOqH0zkG+K67D5Ik4fN5pzeeSpJEfv5OqqpqyczMue7+RBI+n4/TFy5Qf+oU\nVy5fDlmRjlIqKd+1i9qqKrYplVs6hcfpdGJuaOB4Rwe2sTFiNRrKiorCajNERUWJVB7BVUcE/QKB\nQLBBKS7ORafzoVAo1mXV1OFwYDZfRqmUqK7OAWKuiR2XawynMwpZDgZGPp+TGQ2f60tVVS2nT1/A\nZDJRWVkTsdVsrxdj4+OcbGriZHMzjolQAdX4uDhqKiupLi8nLnYy9WmLpvAEAgH++NJLNJ49i3ds\nbHpSNOF0cr6tjfJdu9bZQ8FWQAT9AoFAsIGZW+TpelJf343PV4Qsy5w40UJ6+jbmZiIEFYtWPyHR\n6w28+c0WTp+GuLjgMYdDg1YbtwbPF8ZqtfDaa8dQqZwcOfKusPbt29O5557/ybZtW7vaaf/gAK/U\nmznb0oI/EAhpS0tOpu7AAUp27kSpXN8qyJGCQqFg1GrF65t5QpWfnU2dyURBbu46eibYSoigXyAQ\nCARLYrHYOHasj/FxiRtuSCE52YgsS0hS8D+73cb58xoSE3X4/V5aWq7gcDh473u1VFaWAtDTM8DQ\n0Bg7dqQRF7e8oF2hUKDTJaBQ2JFl7+Qx+ao+2ZBlmZ6eLszmejo6WpiYkImNlairu2HelXyVamsG\n/IFAgPb2C5jN9Qy0nw0ppCVJEsUFBdRVV5OpViMlJi480BalzmSiq6eHil27qD14kJSk67snRSDY\nkEG/KM61MWwKfyLPpvAn8mxuFH/eeGMAj6cYux1ef/0CBw8ayc1Npbn5ApIkk5+fxsWL8YCRy5f7\n8PlKABsnTrSRmSlz7twQ3d2gVhfQ0nKem26aWQVeshCUc6Z4WJCZwmFrvU5ZlnnqqZ/R13dpus3h\nCE42Wlp62bEjPOiPsHo7qx53uQW2XG4Xp88303jajH3MBswU0lJHR2PatYua0lIMOt2M0YU2n26W\nD8o87fbxcY6fPo0sy9w8j5Z+gV7Pp++6i9ixMVAoVh7MRNo9iDR/1sNmpPmzBBsy6BfFuTaOTeFP\n5NkU/kSezWvlj8EQLGw1G73eAChWPKbRCHa7n0BAIiFBxmgEo9FAfn5QJ99iGUWvh/h40OsVuFw+\nFArQ6/0kJkpI0hiJiTkoFBITE/FotV7UauWiNmdQYFzkhLXdd4m8vO1YLJcAiI2NY8+ePezfv5u4\nuKBUaEdHK3/60xN4J2Un4+JSufvu9xIfr70G/qyO1Yy7VIGtEeBYQwONZ89OS25qJ1f3E/UJ1O7f\nT2VJCdHR8zz52EgflDXa7Onro95s5lxbG4FAgCilkn2VlcTN6SsBsbC2ynCRdg8izZ/1sBlp/oji\nXAKBQLD1sNms/PGPdtTq4Aqsy2XnyBGAlUeXu3fn0tjYgVIJtbXz5yC7XHbGx8FgiGJ8vAObzUZN\nTRoA+fnpNDZeYGIijrQ0N2q1erWXtWo8Hs+8AWpVVS09PV1UVtZQVFSK3R5FXBycOlXP888/TV7e\nDu699+/QTG6+PH36Ej/5yXfw+bzce+/fTU6kNg6yLBOYk4c/u62zuzsouTnPimJeVhZ1JhOFBsOW\nT+EJBAL87Fe/mq4uPH1clunq72dXxuZW1BJsPETQLxAIBJsYtVpHfPzaFWaio6OpqdnB6CjMF6/r\n9QYOHQKDAUABpAKp0wFxTIyaw4d34fV6518ZvkYEAgE6Olowm+vx+/28//0fDWnv7e2mqekkGk0s\n/f09ZGfnATqee+5pBgZ6efDBr4SNmZ6ewyc+8Vnsdhvf/OY/cv/9D5KcvO06XdHqGBuz8+STv6C/\nv4eYGDUKhYKAfYS4WIl3v+1t7MjLm5bcHBwZAadzWmFmSnKzzmSayUPfoio8s1EoFBh0Orom/9ao\n1VSXlbGnshK97/pLygoESyGCfoFAIBCsGYVCgV5vJCFh4XMkSbpuAb/L5aKxsYGOjmPY7TMpTn19\nPaSlZXDs2Es0Nb1IenoWtbUH0GhisVpH+dnPvkdbWycZGdt44IEvLWpDp9Pz4IP/P1/96mf54he/\nsaSSkizLHD36Ig0Nx5BlmaioKN785rdSWlp1Va55IZ577necPm3mve/9MBkZ2dPHY0b7CASG+dZ/\n/AfHzGb2VleHXIM2Lo6aqiqqy8uJ3eKVYP1+/7xKRLVVVfQODFBnMlFeXDzz/haTIkEEIoJ+gUAg\n2MS4XPY5/9atnzPXCVmW+eUvf0RPz9DsGlEYDEbcbhf//u/fIiEhl8985ssh1U4zM3MoKzPxhS98\nlvh4HS+//CduvPHworY0Gg233343L730DLfc8o4F/fnFL35Ea2snhw7dxCc+8VkUCgUej4dnn32K\n3//+N+zYUccdd9x2Va5/Ni+++CRKpYN/+Id/DDne39/L6aPP0N3XSrRKRcWuXbz42mvcfPAgGdu3\ns7eggF179mxpyU1Zluno7KTebEapVHL37beHnZOWmsrf3nOPqJor2BCIoF8gEAg2KXq9YTKHfwod\ner0Bq3WhHpsDSZIoKamkp+d5ADIzczGZ6sjLK+Sxx35IZeUeCgr2zyswMzDQx7Zt6Xz84/fz059+\nl5Mn/8Lu3UHpIK/XOxkEh8qFVlXV8PDD/3veoN/v9/P1r3+R2257D4cPfzRk/110dDS33XYXt912\nF0888Xt++tPvcc89/+Oq3YeRkSE6Os7y4IP/GwimOrW1ncdsrqev7zJK59i07KZep+Pu22/HPjbG\n33zgA0gWC2zRgN/j8dB8/jz1r77KsNsNBN9TI6OjJM6zgVIE/IKNggj6BQKBYJMSTLkxhCj42GxW\nbDYwGNZWNCsS8Pl8jI5aMBqTw9pKS01cvjzKgQM10/n2NpsVu91KTc3+BbMvXnjh99x887sB+NCH\n/gcPP/y/2b17L5fPNqFsO49bqURRclOImpAkSSQkJOJwjAPxIeP94Aff5M47P0hBQdGiGR833HAr\nTU1/4qmnfsU73/neld2IBXj88cd497v/BqfTyZkzZhoajjM2Kbk5hTomhurycvZUVGDQ6/n8176G\nLMts1TBWlmX+7T//E4vNFrKvQRsXh21sbN6gXyDYKIigXyAQCDYxcxV8AEZG7Nx5J9OFpxwOB42N\nPSgUkJeXC6wt7z4QCJcKDQSCUqGrZWxsnEuXrpCenkh0tJKmppM0N5/E54vh/vs/EbbaqtFoePOb\nbwtZWX/iice4446/WtSOxTJCQkJwEjH1xODMmUZ0nW3kxgYLih3vOEt23oGQfkZjElbrKBrNTNA/\nPDyIShVNQUHRsq7xxhsP87WvfZ5bb71zzWk1sizT29vN2bMneOaZRny+GT1+i8VOTEw8t+07wKED\nlSH7LG4+eJAXXn2VW8rK1mR/oyJJEsWFhbxx8iQAmWlp1FZVUVxYuKVTnQSbgw0Z9IviXBvDpvAn\n8mwKfyLP5rX2x2YDv1/HbJlOtxusVpDl4N9Hj3bj9xchywG6u9u5+eadq7YZCATo7e3kz38eQ62e\n0rGXqKoChWL+VdKl7kFPj5vTp3txueJobf0FGk3v9IZTh8OB2dxKbm64z3PHvXx5kJiY9OmiXsHJ\nySjNzR2MjytITY3G4Qhw+bKTqKig77W1t/PjHz/C4aJKUtw+xr1e7IaEsN+h0VEXTqeGsbGZY//1\nX49x220fnD53tj+BQCDkSctU2+7dR3j66Wc5ePBtIeMbGVn4Bs0qpCXLMl09l3it/lXazzcT5Vai\niZuZEKlj9OQkv5P0tDKs3a8RPT4eMtTevDz+5dFHuWXbEmpEG/yD4vf7GXM4MLhcYW012dmMDQxQ\nm5ZGRklJ8KDNFnbeRr8Hm86f9bAZaf4swYYM+kVxro1jU/gTeTaFP5Fn81r6I0kQFxcsmjWFXh+U\n1pxS2omLk5BlCVDi863NH0mycuKEk4GBBDQaLR6PnZ07/SQnr/46bDYHGo2RM2d+i8VymZgYNxqN\nFkmSyMsrJjNTv+jvgsMxzlNP/ZKmpqM8+uhXkGWZsTEvSUnx2O17GB8vRKXScP78MFFRGXR0PEd1\n9bsnR4hGo4GSt99C3/lmouN1lCTsCLNnt/eQm2vEZpu5lkDATn5+Ush5Op2PV15pYXxcTUqKl717\nZ54CGI3wlrfs41vf+hK33x4a9KfhXvD+pDGKV6sN5qGbzQyNjDDucBCvUZAS5yAqPp6KXbuoNZlo\n6RjDaisCZCY8sWE3XiXLeFUqSEnZlB+UCaeTU83NHG9oQKfV8tHDh5Hm9DMYjbw7J2fpIlpr8TcS\nvyw2gz/rYTPS/BHFuQQCgWDrMlvBZ+bvmXSfysoUGhouoFDIlJZmrtmeWq1FrTbOWum3rGk8rdaA\nwXCetLQ8rNZmEhPTqazcQ2VlDX6/YdHfvyef/AWdne3ccccHGBwc4JOfDG5qHR2FK1dO8/DDj+L1\nZlFZeT+S5GHHjlswm7/Oe97z7pBxoqOjyanYPd13NqOjI+h0hrD0D4UiPB2ku3uAiYlcNJpYBgYG\nsNtt6HT66XZJklAql//TbB8b40R9PSc7O3HOWrVWx8Tg9/u5qbaW6v37pyU3o1UxPH/0PH6/mqKc\n8AJdIxYLet3mU3gaHB7mmNlM07lz+Px+AMYcDnquXCFzixcZE2wdRNAvEAgEm5hwBR+wWnUhVWST\nk43cckswcr5a8uJe7wQALtcYbvcYsLCAfyAQwGKxTvo2itU6SkXF7un0F4VCwY037mLPnkza2w3s\n2lVBTEzMkv4++eRPychI5EMf+ji//e1/8dprLwIgywGMxlze+tabeOc7H+TkyT9z+vRXOHz446hU\nkJqaSVvbeQoLi5mYcCypv//zn/+Q97znnmXdF70+Fr/fAsSiUIwRE7NIYYNF6Onro95s5lxbGwGH\ng9napOmpqdSZTPj9fvabTEiz2gx6Le9+exx+vx/VWHiVtf9++mneecstq/IpUpFlmZ8/8QRWe+jk\ntzA3lyiRpy/YQoigXyAQCDYxCoViesPuFLIM11K4R6OJo7o6GJQ7HLB/v5b4eMOC59vtFv7wh7P0\n919gcPAikgRf+Uo2KSkzeeWSJKHV6qiqqlmWD+fONeNw2Dl9+hKXLgVX+nU6A7t37yM/fwcnT57n\nJz/5Dj09MbzlLfcjSRZ6e/9CRkYJt976V/ziF49w772f4OjR57n11rsWtPPUU78iJ6eAlJTUsDaP\nxx1Uwpm1yTgx0Uh19QADA21UVydNT15m7oUt7NgUfr+f821t1JvN9Mx5hK9QKNhVWEidyURGWhoA\nB/v7eb2hgf2HDoWdO59ykyzLXO7rIzM9fVMVl5IkiZrKSp47ehRVVBSVJSXUmkwkGY2b6joFgqUQ\nQb9AIBAIripu9zhq9dQqvYxen4Aszx9knj3byLPPvsD588NER2uIjlbj8ThpazsXEvSvlCee+DkW\nyzif+9w/YTQG0zfe8Y738L3vPcI//MM/kpdXzGc+82WOH3+d5577Kp/61Gf4zne+ypEje/H7DXzm\nM1/hW9/6EufONfOud30gbPze3m5+/eufUlRUxpEj75rXh9raA9TXH2Xv3htCjmdmppKZGT5JgKDM\n5jve8b6QY06nk9dOH+d4QwP2ORtvNWo11UVF7DlwICwt561vfjOfevBBqvbtIy42dvEbBvzg0Ud5\n19zHQhsIm92O1W4nOyMjrM1UVoYMmEpL0Wzx6sKCrYsI+gUCgUBw1VhJQTBJkmhpOYvFMjx9TK3W\nkpOzm5ycglX7MDZmp6Ghnq9//QmMxpn0GbVag9GYxo9+9ANuv/0+FAoFdXUH0Gp1/P73/01WVi4e\njxuVSoFKpSI6OpoPfejj/Nu/fZ1AwI9KFU0gEMBqdZGXl8FHP/r3aLUL578fOHCIL3/5f1FXd3BZ\nBZycTif9/T2kpQWD1pGRIczmY5w714TGF7oinWQ0UmcyUV5cHFTgmScPX6FQ8IWPfYzPPfww//jp\nT5NgWPhpyw8fe4zUlBT2VFYu6WckMfV0ov6VV7hw5Qp6rZZP3Htv2JMMtVrNm/bsWScvBYLIQAT9\nAoFAIFgzU9r8s2NbvX7pAmAmUx0NDafR61PZtesm0tNLmJiwsYyF6QV5/vnfYzLtRa8PzZdvbr5I\nUtL7OH78NzzyyBf5x3/8HBqNhpKSCv70pye48cYjnDnTQGamgh/84F+49da7KCsz8Za3HJm+RkmS\nsFikJQU7IBh033HHB/jud7/G3/7tZ2CRklder4d//dfP8/GPP0BnZztmcz2XLrWHnVeQk0OdyUR+\nTs6yJhJJCQn8nwce4P/+x3+gUCi4+/bbycvOBsDpcvHf//3ftHR08PabbmLfBgqKZVnm9KRaUd+V\nK9OFtCw2G60XL1JUsPpJo0CwWRFBv0AgEAjWjM1m5Ve/6gKiMRpjiI+P4siRYODf1nae3l4bb3nL\nvrB+OTn53HbbX9HeHo9Go2diwhamLrRSXnnlWd72tjux2UZDJiFXrkzg9wd405vu4dKlF/j+978x\nXYArNTWDRx/9PmNjdioq2vnYx/4nRmMiQ0NXePzxxxgbsxEVpZq8Vjc5OWnccccHFl3pBygtrcLn\n8/HlL/8v3va2j2I0htYTkGWZkyf/wi9+8RhHjhzhD3/4DaOjwyHnREWp2L2rnFqTieRVKM3otFq+\n8KlPMe5w8Ounn+YXTz4ZHNfj4fZ3vYu/vmvhPQuRzF9OnaJ/lmZ5rEbD7vJy0lPnT50SCLY6GzLo\nF8W5NoZN4U/k2RT+LM9mIBBgbCw0H0WrNTA8vPCq9Wa5B6v1p6fHTldXPKmphXR2DpORYeHo0WN0\ndl5gfNyGyxXFjh0VxE5WtZ1BQqstYd++2fdbh99vmLeo1XL88XoVDL3wPPltY/gm1TBH7FYaHbkM\nDkqo1YO86U07qa29Badzgq6uVlJTExgd/RN33nkfhYVvRZZlvv3t/4ssy9x661+TkJAUYtPv7+a7\n3/1X0tNzefvb3z/d5hy0A94Qfyqz8tj5kU/yq988yXO/+z7aOC2xmljs43ZsYzb0Wj2p8Qaajx0N\n6Rcfp6WqzETZrgry1ZP3Z6U/gLPa4oF7ZyvzDA4G9b434I+qBNQVFPBEVxcpRiN1hYWU1daiUqnA\n4wm9pq3+4dwq/qyHzUjzZwk2ZNAvinNtHJvCn8izKfxZ2qYkWfnLX+yo1cFVXJfLzpEjkJJi3BL3\nYDX+WCxeYmNVqNUyly8fpb//DSwWJRqNZlJN0sfg4BlMptp5RlRgXMLoSvzNyNjG5Y5W3rtXO12U\nbGxsiNzcKkpKjDgcWkpLp17LWNLTg3ns//7vD3Ho0AGcTvj5z7/GDTccomJSmx9mUphiYsBgiOdj\nH/s4J068xvPP/5T3vvceAGIYJ3FeXxV87q/eDMY78Hq9tHV2cubCBS50dBAIuIn1O6dlNzO2b6fO\nZKK4sHCW9n+EvUmug01Zlmm7eJEJp5PKtLSwfqU1NWjT0sjNykKyWDblPRD+bACbkeb1CeT9AAAg\nAElEQVSPKM4lEAgEK0Ot1hEfv4zE7U3OVKA7hc0WrPI7N1/faEwkIaGPQKCf2NhRJiZm9M+zs/PJ\nz6+jsvL65FkrFApi1RosDjvx8cGJm14fj8fTi0ajJy5uHLU6VOHF6QzWFdBoYnn++T+xa1dFSMAP\nwRSmP/7Rjt+vIy5uajK4n6ef/vW0rv9i+P1+zl+4ML/kpiSxa+fOEMnNrYrH46Hx7FmONTQwYrGg\nUaspufNOVHPOUyqV0/sTBALB0oigXyAQCAQLMhXoTj31cDhAqQw++Zir/79tWzyJiWpSU/fx6qvn\nKSmp5sCBQyQlpTA6CsvYd3pV8Hg8vLd6Hz96+Xc89N4PIEkSGo2GA7uT8PlGSE8vwOEILcr0jW88\nxP79NwFgNr/KQw99ed6xg/fBOP0EAeA977mHf/u3R/jUp744b58Jp5NTzc2ceOMN7IHQKrgatZrq\nsjJqcnLQZWWt/qI3AbIs8/wrr2A+fRqX2z193Olycbajg8ptq5dwFQgEIugXCASCeQluJp397/AN\nmz6fb8lqrdeT2avyUyvysDwVncWY+9TD6bRTX/8KkiRx+PDt0zYOHYKgKmQqR478AykpqWuyu1oO\nHLiZky/+ntqCUr7y20f5+C3vxOFxodVqpycqDkfwXFmW+eEP/wWrdZRPf/ohuroukp6eG6aM4/f7\n6Wu/gHVQjz45gdlKPGq1GqUyCodjnNlltYZGRqg/dYrm8+fx+nzTCjMAyYmJ05KbKpVKFIkiKOE6\nNDISEvBnpadTZzJRtBy5JIFAsCiR82slEAgEEcJSWvMej4eXX27F5Ypj2zY3dXVF6+FmGLNX5R0O\nZqWghK/KrwaLpZezZ+vp7z9JdnaA2NhYamsPkpBgRKFQoNcbSUhYepzlsJYJzMGDh/j8c0/x4Tv/\nBp39Io/87tdkZ+fzvujo6XPcbhe//e1vaG09R03NftxuF3Fx8bz88p+oqtofNualY6+yvecSUZ1x\n9FgmyNyZHzIZLC4up7OznYTtybRd7KTebKajqytsnMLcXOpMJvKys5clubnVqDOZuNjVRclkqlPa\nlBKPmBQJBGtGBP0CgUAwB4VCsWiQ3NHRi9+/E41GRX9/z2Q++BqE5a8is1flZ6egrAWXy86JE//N\n0NBFPB5QKNxADJIk0dvbFXKvXC4X9fWvYLGMEBOjpri4nNzclefyr2UCo1Qquf/+z/K9732Thx76\nCrt376Ozs52f/OQ7+P1+ZFnG7Vbwrnfdzv79N/G97z3C5z738KT/TtTq8IqtUWN2tukSuLPWQaOv\nm+Ib85maDAJERUXR3HwK88v9uDyWkL6qqCgqS0qozc0lKT9/xfdiM+GYmOBkSwter5dDBw+Gtedl\nZ/Op++4jPm6uypNAIFgrIugXCASCFZKQEE9r6whRUakolWOoVKk4nevt1bVh6qmHTpdEY+NFXC7Q\n63Xs2fMmTKY64uO1WCyj9Pf38utf/5Lo6AA333wrubmFuN0ujh9/lV/+8sfs2FHHHXfctqLV7bVM\nYNLTM/nABz7Jww9/jurqvbz1re/k/vsfnG7v7rbz5z//nMHBfj772a8SExMzeb0JkxWCQzeIqvIK\n6T7bhD9KSfLOPdMTD7vdRmPjcX7zm//EYDCSrIkhdnLOoNdq2VNZSXVZGRqNZkuvVg8MDnKsoYHT\nZjO+6GiilEr27t5N3JwqbJIkiYBfILhGiKBfIBAIVkhqajLV1QMMDrZRUJARUXn9U3sRpnLWV1ro\nyuv1wiydlKmnHgcO3MTw8BXy82uorS0nejJVxmIZ5Qc/eIW2tr+wb98n0Gj8FBQEV8BtNiu33PJO\nAF588Sjf+taX+Pu//8J1y/NPTc3gi1/8Bs3Np/iXf/k/qFTRyLKMLAfw+WJ4//vfT3p6Zkifffve\nzHe+820OHKgOOZ5WWIw7Kw+FQoHdHkVvbzenTtXT0XGBQCBAX18PubmF4BonMy1tWnJzPfY0RBKy\nLPPY44/TfulS8IDfH/xfIMDFri7KihdXPBIIBFePyPmlWgEbsI7IlrQp/Ik8m8Kfq2czNjaVnJxU\nfL7gd9J6+wMQCBjYuzf476EhSE6GuYWu5uvncjl56aXX6OxsQq3Wcu+9982zIm/gtts+ztCQxPj4\nzNHTp9u4cOEEt932NWw2Cb9/FKsVrFYrL7wwo/ozOFhJZWUy3/zm1/nIRz6z5DXabDAyYsfhCP5b\nrw9OYKxWHbK8svuTkVHNvfeGBvHOQTvpGi+MjkzeuwC2MRsAjsEBBi6cJSYmBr1WPx24R/n9tLRf\n4NXXjzPumjE8Nj6ONkZNcWYue9PSKC2ZlNy0hhZ4u1YFdyL5gyIB8YEAU4/Cot1uqsrKqC0vx6jX\nX50iWmvpu5FsCn8iz2ak+bMEGzLoF8W5No5N4U/k2RT+RJ5NgyGA3W5Do4mdTjNZ3bgKkpKCB/T6\npft5vV7OnWviV796CotFgSzLKBSXcLl6wlbAg0hIUui4f/7zr7n11gfRaqXpzbZBBR9ITAxV/TGZ\ndPT3n8XhuERmZs6i12gwGLjzzuC/rdapMXWTG3kXuwfLa5tbRGvUYuFPf/EQq9ayK+NmHvmXn3Lo\n4Pu544ibmJiYoORmYyNjDgeSy4l2UoVHo1bT09PDj7/5FbLS04NB7LV6Yy5GBHxQAoHAzJONWW11\nN9xAl81GTWUlVenpqLdvvy7+XPO29bAp/Ik8m5HmjyjOJRAIBJGJLMscPXqe0dEUFIpL3HjjdvT6\n5afjrIVf/vLHDA72Y7VakKQkJEkiOjoep9OxrP4DA30kJaXg8TgYH1dOa/gvlk50++1385OffIdP\nfOKzi449ezO1LHPVVIEWI1atRRufgDa7nAnJx59e/inqmAIuXb6MbzItZYqUxER2V1Tw+DPP8MVP\nfSoY8G9BZFmmq6eHerMZWZa5+/bbw85JTUnh7z7ykeDToy28r0EgWG9E0C8QCATriMfjxmrVER+f\njCwncelSBxUV1yfoLyoqY3Cwn5QUDW63kvz8Cm64YT8FBctbcX766V9z990fnU4FCq7I67BYJmhq\n6qO1VUNlpR6FYqYQVny8FrfbdU2u52ogyzJ9gxcZsfQzar3Cd37yKqnJyZTs3IlKpWJHXh5Fycm8\n3trKU88+y8c++EFyt2BRLZ/Px5kzZ6g3mxkYGpo+PjI6SuI85wt5UoFg/RFBv0AgEKwj0dExaDQ2\n3G4jPt8g6ekrS/Hwer3TGvPzBVZ+vx+bzYrRGB6KlZWZGB6+QkXFHtLSMoCVLcQ6HOMh48oy6HR+\njh4dQZYLsNlGaGlpITMzNWRDcUyMOuIKm3m8Xs62munpb2PcMgjR0cTH6dlTUYfX56PvyhW2/z/2\nzjy8qWrrw+9JmjRJpzSdB+g8QVs6QpEWZBRQEEFArniviopeP1EUBBVUFEEURb2OOF0nBEEmr4Ao\ng8wIlFnmzqWldErbNGma5Hx/BEpLCwUBKXre5/GRZp+91zonabrOPmutn7c3Wbm5FOfkMPKuuwi4\nWJrKXxhRFPlo4UJOm5revDlpNJRXVuJxNr9LQkKiTdF2vnElJCQk/oYIgkCvXlEUFpbg7u5xSak9\nBkMNS5d+y7FjOXh5ueLoqKK6uoraWgNdumTQu/dAjEYjv/2Wye7dvyGXy7n//seadZJRqVQMGHDH\nNTgnGxqNlsREI0FBJURGQmXluZ72TXK/rzOVej2/7dnDrn37sFj0+HoBLmZQyXBzdad7Whop8fGo\nVKpzk1rL2/+LIwgCUcHBnD58GABfLy/SkpKIjY6238hJKTwSEm0SKeiXkJCQuM4oFAqCgy8tJ3zT\nprVs3PgLd9/9IAMGhDTEnmVlFWzYUMLGjRt4663uqFQaoA5BsAfXWVlHGTnyPjp1Srlqfmu1Ok6d\nKsLH59yOt1wuJzVVx9Gjx/HyEkhM7IRMJkMUQSY7kwOee4J33pkB2ANIrTaIkSPvbBpYX0NEUaSg\nqJA1m3Zx6PhxxDPtgNRninPb+/mRlpFBdHh4m7k5uR7YbDZqDAZcXVyajaXGxlJmsdAlKYmgwEAp\nfUdC4gZACvolJCQkbhC2bv2V48cP8cwz9oC58YZqVlYpanUEu3c/R01NPbW1JfTp0x9HR0dCQiJI\nTe3GgQO7Wbr0W4YMeRSdLvSK/bn99rtYsOBzHn74qSav+/l54efn1ez41auXs2XLegAee+wZ5HJ7\nrv+OHb/zwQev4eioYsyYx1vsYHQ1sFqtHD58gMzMbZTlHm0Q0QKQy2R0jIoiLSkJf6Xyb72TX1dX\nx+69e9l+4gRqlYoH7767WVDv5uLCyNtvv04eSkhI/BGkoF9CQqLNYTQaMZmM2Gxa4O+709qYuro6\n1qz5ke7d+7Jo0Vc4ODjg4RFJenoSgiDg6anhww+fJDV1OMXFv+LlpePkyRxeeuktdDpPANq1C6Zv\n30G8/PLzODvfS1hY5BX55O6uo7paj8lkanWX/rvvPiI01B9//3Y8+uikhoAfICysA6mpz1NUVMiM\nGZOZPPkV1GrNRVa7PGprDezdu5O9e3dgMNhFBs5a16jVJMfHEx4cjMsZJdjyykq0Wu3fbpe/vKKC\n3/bsYfeBA9Tp9aBWU6HXU3DyJO3+pt2JJCT+StyQQb8kznVj2JT8aXs2bwR/Kioq2batFFHUIgiH\nGTCgw5/mz7Va90r9qagoZcaMx3Bzc6e+XkO7dgmUlZVw5Egea9Yspl27cOrqjAwdegeenu2oqvLH\n2zuFn356m5oa5XkrOjBixMvMnTuBCRNmNwm+L9Wfxgwa9DDTp7/A44/PoKxM3uKcNWuW4ugYgCi6\nYLNpkMt9WtRkcnQMYPToZ5g582WeeGJmw7ixpAqov4BDVs6F8E0pPVzEjpO7OXTsEFarpcmYl9KR\nPmmJxIaHU11by+KVZjQq+xOG2hI9Q4fm2sWjLuUiXOux1rgKNkVR5Ot58yjX2wXKzko6hwQEIJy5\nAbjaNq/a2N/FpuRP27PZ1vxphRsy6JfEuW4cm5I/bc9mW/cnJ+c0Xl4RAJw+XYOLSz0KheJP8+da\nrftHx3JyjvLjjx/Tvr0/DzzwBJmZ29i3bw1KpSN33vkkRmMXdu7M5NNPp7F48YYzeemhlJfD3Xf/\ng3Xr5jN69EPnrSpn5Mi72L37R/r1G3xZ/pw/rtP5c//9D/D++08zfPgT6HRNRb1EUeTYsW34+3eg\nqCiXhx9+9KJr6nTeJCTEcvr0QaKiOgLNhbQa4085Vje7aq4gCIiiyNETJ9iWmUn20aOgVqM5c98j\nCAKRoaGkJSUR7OSE4HGm81BFBRoPR1ycG4kBaB0uLg6g01FdU8Py1as5eeoUcpmM8JAQBnTqhOIG\nFOcSgNSuXfnp11+Ry2TER0fTpXt3fL29r5nNqzr2d7Ep+dP2bLY1fyRxLgkJiRuFgAB3cnJykMs9\ncHY2XDDg/7Ow2Wzo9ZUA6PUgCJxRhb32qR+nT5/i++8/5h//uJvZs19k/vxPG8ZMJiObN69BLk8m\nO1sgPf0+Xn99ClOnzm7Iv/bx8Scr6wTV1dW4nFeMmZjYmVmzprQY9F8uoaERPPnkC3z99TcsX15A\nhw6d8PT0pq7OxOrVy6mqqqR79xGkpyc1mVdXV3fm/W16LW+99U7ee+/VhqC/JQ4eOcKiH39ErK3F\n0c0Ns9lMTkEBhtpawoKCmtQFKBUKEmNj6ZyQgMfZP5ZX0GGmoLiYT//7X5QKBcMGDiQoMBCrzcbB\nI0eYPncuzp6ePPLPf+J8Jl2oLVFjMFBRWdliuk5ibCzm+nqS4+Nxrqv7W9c1SEj8FZGCfgkJiatG\nfX09mzbtobbWRkyMF+7uWvR6Lis/2sfHk969lVRX1+DoGH2NPW4dvb6SlSurUKlcGxRnBwygQS32\nWjJv3ic88sgL/PbbMmpqqhpeV6s1xMUlYzJ5YLG4k5u7m96978HV9SCbN68jPb0XAL/+eoSTJ5X8\n8ksZ3bqZ8fa272zbbDYqKysBkYqKc8GvvaXmH7uZcXFxZeTIR3B3Fzl+/DAVFWU4OXni5ubOK6+8\nS2Vl03W3bTtMcbEapbKGhIRQ4Fz6yMUKeevq6nj5rbfoEBnJ5EcfpfbkSX7LzSVz/36CAgPRV1Wx\nNTPT3kYyOpou3bqRGBvbas1Bran6vH+3vMu//9Ahvl2wgOefeabZmqkJCaS2b0+JzcZzs2Yx9Ykn\n8GwjgXPRqVNsy8zkwOHDuDg7M27MmGbvtEqlokfXrvYf6ur+dB8lJCSuLVLQLyEhcdXYvn0fP/2k\nRaPxYNu2AhITtZSVVXHnnZcXJLu5ueLm5tpm2n2rVK44O/+5wZvRaMRms6LROJOY2IV58z7Gw8Ob\n5OQ0oqPjUCgUFBfXsXfvYSyWctq3N5OUNIDXXptKenov6uvN6PUqHBwccXIKprDwWEPQX11dydat\nVeTkyFmzxopMJsdkst/MwJWdpyAIRETENPy8bt2qZjd8RqOR4mINGk17bDYrWVk5+PuHNTmmsYrv\nWaxWK1Nee41x99+PTRRZumoVhw8cQGwUfLu5uvKv4cMpq6ignbs7XVNab1GqdXNj6AA9cCbQrRTQ\ntpDPX3TqFN8uXcor48YhXOQmwtvTkxmTJ/Pcq6/y2pQpKJXn11X8OYiiyOGsLLb9/DO5BQUNr1dW\nVXHkxAliPFrSzpWQkPirIgX9EhISVw2r1YZKpUWl0iGT1eLsrDtbDyhxEURRJCvrGBUVZaSk2Hda\n161bSd++9tSb4OAwYmOTGDHiXiorDaxdewKFQiQ6Ophbbonl99+1JCWFIwgCbm7uVFXpUSjcyMr6\nH+HhadTWHiMoqGkLTZXKFQcHJa6uzVtrXg6VlRWUlpbg4OCAQuEHtN51R6lU4uBQjSiKmEwlBAe3\nLkgG8P5//0t6aior1q6l6Gwx25ke+3KZjNjoaNKSkvDz8QFgxqxZlJSW4u3pedF1ZTIZusb5+2dF\nBc7j8wULeHbcOIRL2AV30mi4b+RIlq5axYjBV55C9UfZuGsXJ2tqGn52VCpJiovDz9sbrNbr5peE\nhMSfjxT0S0hIXDViYtqxaVMxolhHSEjby2f+sykvL2PJkm8oLq7EyelcEDl48EhCQyMwm83s37+H\nEye2U1FRhkwmp0OHeDQaJ4qLC0lP743FYt89v+eeh/nf/xbi7NwFB4dozGYbBw4cw98/itTUdDZt\nWktGRm/8/AIpLS1Bq3Wjvv4U999/D2q1ukF46iyiKFJfb/pD52W1Wlm//id++20TXl4+eHv7YbNZ\nOXo0B7m8jgEDhtKhQzwAPj5+5OZm4eJyThdALpfTq1cIx46dwNPTBbW6+Y2HyWRs+HdNrYG9h/aw\nbPVq0jt3bnKck1pNSloaqQkJzXLoHxw2jK8XL2b8Q+cXMl8+JpMJi9Vqt3GJqS+dOnbkux9+uG5B\nvyAIpMXHs3jLFnRaLV0SE0no2PFc+lRbeZQmISHxpyAF/RISElcNhUJJSIjLGTVYkZqackymKuDS\ndnLbKvZzoCGnv7Xzqa+v58MPZ6NQKBk69G4cHHwbaiKNxlqWL1/A669PJSQkArNZ3tAN0WazcvDg\nXlJTb0Imk2NttBMbFhbJ999/RWBgOzw9o7HZrCiV9huJbt16MmvWFDIyemOzWZHL5axatYC0tO7o\nLpBT/vvvPxMUlERNTXmjc2z9faqpqeLtt1/g1luHMXnyK01Em8rLwdnZzLJl89mwYTVjxz7F4MEj\n+fTTdxg9+tkm62g0Gjp1Cm+Y15j9+zOJjU2kpKSYzMxtHMvcTGFxNoF+55R/fTw9SUtOJlKrpais\njIriYpzDmqYIeel0VOj12Gy2Ky68/mXjRgb26tXs9VMFBVTs3YtNJiOwa9dmVzAoMJDCoiICGvl+\nNRFFkez8fKqLiujUsXnhc8fwcFTe3oSHhPztdAckJCSaIgX9EhISVw03N+2ZvPBzVFa6nikQ/XM4\n222nsYDolXTbaXxOlZWg1V78fCwWC6+++iz/+te/ad8+BGga1KrVGkaOvA+VSs3HH79FfHw6arUz\nAQHtSUpKIzzcXrwcHBzG8eOHCAvr1jD38cenMG3aBBITrQQFBREcbN89FwSB8PBoduzYTGFhHvX1\nZsxmGT179m/RR0dHNdXV65g06ZVGWSz286qsvPC1qKur4733nueZZ15Aq2250FWpVDJ8+D/Zu3cn\nc+fOYezYJxFFGzt2rEejsaJUKgkNjcTLy6fF+Varlc8++w+pqel89dWHAMhtNopKSkhLSiI6PJy0\npCSCAgMRBIGDixcTrdFQU19PntVK+8imgmM+Xl7oq6pw117ZZ/BUaSkpnTo1e73ywAGiz3SYOnLg\nAK4dmupKBPj6cqq09KoH/fX19ew/fJhtu3ZRUlCASqslJiKiWf2AXC4n8rybIQkJib8nN2TQL4lz\n3Rg2JX/ans1r74+M8wtB6+q4YCB5LfzR6ytZvLgKb2/7nqvJVEWfPuDmpvuDNs+dU12dPd27pfM5\nO++LL95m8OCHcXYOafiuaslmdHQPOnf+nX379jJ9+gd4e/sD59bu2LEPH3wwjREjujWa5cgTT8xh\nwYL3+f33DcTF3UqPHl0QBIGbbrqTyZNHk59/gokT3yA+vneL35Umk5FZs6YwduxUBMHzbEp8g+2L\nXZ9vvpnLwIHjsdncW1y7sYhWp3Yh7Nv6K7OmPUl9vYXP10/n4fvHYlM68r8F/+VU6Sm6d+3BTanp\nUGKlzmxh/+/7ePeTt/D08KIo68i5szab8Xd35/Fhw/A8m3tfUQGAoqQEubc3bsCp3FxonL9fUoLK\nYsF06hTYbE2dvcwPl6KujvrSUnBwaDJuM5uxmc1YbDZkCkWzufUVFShdXOx/uK6C4I4oiqzfsYMd\nBw5QazqTnmUwYAL2b99O8vm7/W33y+LG9ud62JT8aXs225o/rXBDBv2SONeNY1Pyp+3ZbOv+iKJI\nZuZxKivB1dWR6Oj2l7WuIIC3tys+PvbBmhrQapvqLF3Na3D2yYKjI9TVGamrKyMuLgSTycDevTup\nqqokNfX2ZvN0Oj8mTpzM8uUrMZuL0On8z7Mkx8dHi0xWjE7n2+h1Bx57bBxWq5WlS3/k669nIIoi\nCoWCrl270qFDNIcPb8LLy4/o6HO7zgZDDcuWzScn5ziPPjqRyEhfLkRL52+z2aitPUVcXNAFr09j\nEa3cggIKC3/HQS7n7ekvUpWXx8tffEHX5GQmPTwKmUzGj7/8wntzp9EtJoafdu5kx969dIyMJMDP\nG7Di7uZGl8REEgMC+HTVKkSttplzirg4sk6fxiSXE9i5M5zXdae8vh5t+/bNFWUvdKIXGIvu1Ik9\nBQW0O7uTf2Y8pF8/ju/bh0ypJDQuzi7m0GjusdJSevXvD2d1Eq5QnEsAioxGagWh4Zz8PD3p2r07\nHaOiQN6COvFf9cvi72hT8qft2Wxr/kjiXBISEjcK+flFFBZ6o1K5cfhwHrGxtajVrXeEuV7o9ZWY\nVy7G2arhpwMbSHTVsmTJPPLzc7BaLQAEB3ehrk5Aq3Vpci5qtYabbx7Ml1++THx8crO177lnLC++\n+CwvvjgDjaZpkapcLqdnz8HodPYi0SNHDrJy5RImT34Fk8nEggWL2LRpEYJgV6qVyWQMHjySf/zj\ngT9Uv7lly3rS03s3e91gMLB3bwFqtZzUQEdATml5Oe98+ilvPP88r3/wAUajEVdnZ16fOpWde/fy\n0pw5VNXUcLK4mNyCAr75/ntSEhLonZ6OXC4nODDQnq8fGmpPyyovZ3C/fiz84Qf+fe+9TeyHxMRQ\n37kzcrm8WQqXKIpU6vXNipj/CKkJCUyZNYtB/fo1eV2lUhF5XnHxWerr66kxGHA9TxjtSklLSuJY\ndjYx4eGkJSfTTqU6py4sISEhcQGkoF9CQqJN0bgwVBDEixx5YUymKs52KfwzColdVBoUuLAicxu1\ndXWwYzMKhQJRFLFaLZw8KSMj43GggN69/XFxcW6Yq1AoUCha7uOu0TgxduxUXn31OUaNGkNMTBxg\nb5O5fPkCTp4sQ6m0UlCQg7u7B889NwuwB6KDBo1ucTPIYrGQlXWcvLxqnJ1dCQkJR97S7vB5ZGUd\n5bbbhjd7ffPmXEQxhsrKevbod9Hv5iA+/PJLpk2YgEKhILhdO4pKSgh1ccFisSCXy/Hz9kYmk+Hm\n4kKHyEj27N6Nv68vyfHxpCUl4evt3cxOoL8/hcXFiKLY5DNy9hq2xJY9e+iWmtrquV0KgiAQGhTE\n70eP0qGVFqBnWfi//zH4vJuES8FkMpG5fz9Gk4neGRnNxkPat+eJBx7AzfXM51rqwiMhIXEJSEG/\nhIREm6JdOz/Kyo5TWVlCTIz6snf53dy09OljT+mx07Tw1mq18uuvR6iqUuDnJ5KSEtniOpdDWbWe\nT37+DqvNRufwaGyhkbi6uhEbm0hYWBRz525m2bJp9O07jqKisiZBf2u4u3syZcprrFjxPd9++wl5\nedl4evrQpUsGKpURs7mc224bTl5eFhMnPoizswvu7p6YTAJhYe247bbhqNVqTp0qYsmSb6ipqSYg\nIBY/P1fy8rJZsuQbXF21DBs2Gk/P5sH2uetmD9jPb+1utcqRywXkcgWmOiu1RnurzbPtMxUODlTq\n9aw7dIid2dkYamsb5gqCgLOTE4/edRfrDx5kSP+WC4/PMnTgQD788kse+de/Wr1uhtpaFq5ezRsz\nZrR67KXyr+HDmfTKK0y46y78Wnmkv3PvXgqLi/nHHXdc8vpl5eVs372bPQcPYq6vRy6T0SUpqVkr\nUkEQzgX8EhISEpeIFPRLSEi0ORISWm7leCnIZDLc3HRNcvgbU1BQRFVVEI6OTuTnFxEVVYWLy+UF\nUFartWF3vLKynI9XL2fyoDF8tG4Bzo5qorv1omvXHqhUKqxWK0lJtSQk9GX58sl07Tq12XqiePEn\nGkqlksTELuzfn8nUqbOpqqpEoVCQkhJESIiOX39dTVFRITfffAvHjx8hPb03oQiBalEAACAASURB\nVKFdKS8/wocfziY/PwdfX38efHA87u46yssbp4SOoLy8lM8++w/p6b2JjOzeog9eXr4UFRXg7d20\nC07Hjm4cOHAYBwcbnWJ8WbpqFXfedhtgV7BdtX49Pp6eWKqqqK2uBgcHdAEB+Pv40DU5mY5RUThU\nVbH12LEm17UlkuPjKSwu5qOvvmLsPfdc8LhKvZ4X33iD5x588JKeYlwqDg4OTH/6aZ6fPp2evXrR\nv2fPZilFpro65s2fT3llJRMefviS1hVFkYWrVvH7ebm4VpuNY9nZJMbGXrVzkJCQ+PsiBf0SEhJ/\nK5yc1Nhs1YATgmBAoWh6d2Cz2diwYQ07d25BJpMjCAIKhYLBg0dSWQkbN27DYKhm1KgxCILA55+/\ny4N9bqWu3oy/1pMQHz9iElJRqVSAPfc+IyMaQaigb985vPfeqw1pOGDP+z6b+38hDIYaPv/8XaZM\neQ0Hh3Nf2+XlsHDhlyiVjjzzjH1HWxRF5sx5CZXKn9jYKNLTe7Ft26+cPl3SZG5jdDpPJkyYxkcf\nvUl9vYaMjJRmx/TqNYCPP36Le+5pGoC2b+9L+/b2omDH8pNk5+cTFx3NZ/Pnk1tQwNGsLNr5+6Mv\nLsZbrSZMq6Vdp05079u3SZqOt4cHlXo9Hq3soA/u14+tO3cyZdYswoODGZmRwdmM/azcXOYvW4bV\nauWliRNxra+/6Fp/BLVazWtPPsnmEyd4/vXXcXF2xtfLC6vNRmFREWJtLcNHjCAmIuKS1xQEAcdG\nrTYd5HLiO3QgLSmpVTVhCQkJiUtFCvolJCT+8pjNZqxWK2q1Gk9PD+LjCykpOUanTu4NwTnAypVL\n2L59F/369WH8+OcRBAGLxcLu3duZOfNZcnPzSE3tjLOzCydP5mOz2QgPj0I7yH5D0LvvzXz++buk\nndfH38HBAZ3OrjobGdmRY8cOERERA8DatUvp3//iKSBLl37Lvfc+isFQg9Vqxc1Ni1wuZ8uW1SiV\njtx++8iGYwVB4JFHJvLmm7Px93+U5csXMH78C+j1FUyb9hSTJ8+gulqOIDTXL3joofFMnTqZ9PTk\nZnnzGo0TNpvtjFJu88LY/PwcPnnnJdZvWceajRsBOF1WBoJARWUlEb6+DO7YkUB3d/K8vJqtLwgC\nl1rB0TUlha4pKRw9cYJ3vvmGeoUCQRDw8/Fh3Jgx59JhrlGuuyAIpHfuTHrnztQajZSVlyOXy/HU\n6VDW1Fy0s0ZLNQkAafHxHC8tJTUhgeT4eJw0bbd4XUJC4sZECvolJCT+0pSUlLF1azk2myNhYRYC\nA0Np186H7Oyf+emnXE6fPoXFUk9JSTEBAe14+OGp+Po6NsxfuPALTp7MJzQ0Am/vEHbtWktaWg9q\naqr5+ecfuPfeR3F2dkEU7bGep6c3+/fvQal0x9/fAze3pqlDgwaN4IMPXmP8+OcxGGo4cOA3Ro1q\nXiB7loKCbJYsmcfJk/l4enojk8kpLz+NxWIhJyefd975pNkctVqD2WxizpxlaDS9WL9ewGAQyM2N\n4uOPtxAYmIFcXsWAAeDufi5AFQSB5OQMdu3aRkpK12brjhx5Hx999DIvvnhOibe8vIzZs18gPz+b\nDkFBdIyKon1AAAKwav1e7rjl/ygsPsbJKj3VMn/6ZphJbiFdpaS0FO1l5qlHhoUxacyYS2p1ea3Q\nqNVoAgJaPa6wqIjtGzdSp1QyasiQZuM+np6Mf+ghSTVXQkLimnFDBv2SONeNYVPyp+3Z/Dv6s3t3\nGVarvVg3MzOTJUteRxCqUak0lJWdol27MPT6AvLyCgAVs2dPw9vbhcGD/4W3tz8+Ph04cSIfALU6\ngAkT3mb58i/RaiOprrZgNrs00V0aMOBBJk16gm7dnsTF5RS9ejlQXd1411aFySQjP7+ad9+dytCh\nU1r8ThNFkfnz36eg4DS33HI3w4Y90GR828aVHNuayeR77+Sf/zedgIhzokz19WYiInoxb94HjBmz\nCHt3dwgIuIPff5+LRp6MYMjjqL+KiPim7SaTI7uxcMlMUkKbFzj7q9T079yXmS+M585BI/j9yAEW\nLl+At6cPCVEdkRuqiAkMZO+RI7g6O/PU6Dn4ebWHDj05fnAL23O28WjUCBTV1U3WNRcWYjUYcKiq\nav0Nvd5jlzHXZrNxKCuLbfv2kV9cDAYDODlxOjYWr/NvVEpK+MPhfhu+Bn9Zf66HTcmftmezrfnT\nCjdk0C+Jc904NiV/2p7Nv5s/ISEqDh8up7q6kk2b3uLpp19n2bL3yMi4ibQ0e9HqzJnP8vHH32Kz\nWZk16xWGDbuXRYve59Zbh3HTTYkYDAUkJKSi0QTh4SHg7i5nx45lqNVNfdDpwGqtZejQmWza9Dlm\ns5GOHW8jODip4bjq6iqOHNnBN9+8yuTJzyOKuhbP47PP3iU1NZEOHRyQy2uaHbPz10XMuet+jNZK\nZs2dwqyPv0cmk7Fr1zHy8xWUlFQhiiZcXM6lkqjVDshkVrwrT+CsMuNVcBhFcnSTQmY/jOhcBPx1\ndc18slgshGpLOeqlZtqsCVTV1ODu5kZIoA5BMOLkqqDUZAWFghnPP8+2XV7gbO9UFN7xJgrFatYf\nOsTQgQObrPv9ihXccccdbevDd4UfTFEU+fjrryk6+wf6jFaASqulVBSbB/2XYvMK/LnqY9fDZlvz\n53rYlPxpezbbmj+SOJeEhMTflcjI9sAJ3n33Fd5550PefvtNhg+/m/DwKOrq6li5cgkFBbksXPgF\nY8aMY/z4V3n//UlMmjSdt956mREj7mXQIHv6zdkd+bi4JH788XscHR2b2dPpdLi6HqZHj7sRxVz2\n7NnMypX/w8npnEhWcHAYkye/0mTNxhw+fAAnJ2c6d05ny5Y9GAylzY6xKRywiSLebu707TmQhQu/\nZOjQuykocECjCUYUT1FXZ2syp76+GpvVgsFYjlwm4ABYWih2Pb+bUHVNDTv37mXnvn0YyspAraZH\n1678vGEDft7e1BqNhAUFERcQwM233EJxSQmvvvcenTtNarJOYseebPrt9SZB/7GsLI7k5DBq9Ojm\nF+IGRhAEwoKCGoJ+D3d30lJT6ZSWhlLZsi6DhISExLVECvolJCT+8mzduppJk6Zx8mQ+Pj6BeHp6\ns27dKg4c2M3Wrb8SG5tIVVUlJ04cwcMjhrFjn2TunGk8FB3PO7Om8OTM93H38mmyppubFpVKTX5+\nDu3aBTe8LpfL6d07BqOxFrW6CzJZ1yYtMktKivnhh+8u6u+PPy7isceeBaBduzCWL/+Jvn1va2rf\nN5C84Cic1WZ69RvE668/j1wuR6GoxWazkZOzAScnF2pq7HcVNpuN6GgLzs4yohLVaC2lWIPjcNc1\nVXIVRZF6i72b0MniYrZlZnLwyBGstqY3EC5OTnRNTmbWc8+da4tZXs72PXv4ZsECSsrKmLZ2JB0i\nuiAIMoK9woiOyyDA15f8wkLaBQSwYds2Vv/6Ky8+9FDrb2IbRRRFDLW1zXrpA3ROTKT49Gm6JCYS\nHhKCUFEBUsAvISFxnZCCfgkJib80oihSVFRAQEA75sx5iZEjJ7JixTcUFRVQXl5KdvYx6uvrcHPT\nodXquPXWUAIC2nM6+wQBqRkEu2nJ3bsD9z7nBd1u7nTtejNLl37LY48902RMJpPh5NSyANfixd8w\nYsS9F/S3ttaAQqFs2A12cnLBaKyluPgkWVnFmM1WYmICCQoKpVoU6RifBEBiYmf27dtFz56xHD2a\nhYNDHqNG3YKf3wGio2MBGdCeDz9U0TGj/wWfDu/YsxNvDw8+mz+fvMLCJmOCIBATGkpajx7kFhQ0\n6auvr6ri5dmzCfHz45Vu3XAQBDYOrGd7bi5VNTX4OBWxfssrxMfE8ObcuWjUalITEnj56aftwfAN\nRn19PfsOHWJbZiYOcjkPjR7drCuPq4sLo4cNu04eSkhISDRFCvolJCT+0hw8uIeEhFREUcRisaBS\nqZHL5axftwpnQSDCN4DRg0YQ5RdIoYOCTz99FTc3R4LCItl54gg9ElJZsXcnCecF/bW1Bry8fFAo\nlBw7dggPj5hWfcnJOYHFUo/uvN31xhQW5hEWFtXktfT0Pjz99PsEB/8fMpmcNWuO8n//14tPPplL\nWlocAHFxyWzevJaEhFRMpiLS0m7mjjsG8Z//zKBrV3vtQlbWMUJDW+4fbzKZ2L8/k7c+eJOMLklN\nusioHB1Jioujc0ICWqsVdDqOnjjRsLtdYzDw/OzZTHvoIUqys3E5kzIUrFbTb/x4LBYLW9etIyQq\nii+++46OUVG8Mnlyq9erLVJVXc1vW7eyKycHo8nU8HpeYSFBgYHX0TMJCQmJiyP1BpOQkPhLYjDU\nkJNzglOniggIaI/RWIurq5afflqIzSYyZuCdfPngeEZ16IRh6wYiRQio0jN27FRGjLiXPfk57HVU\noet/BzZ58/2R0tISnJyceeih8Sxa9CXHjx+8qD8nThzlq68+5OGHJ2Cz2aioKKeiohy93v5/25n0\nGbPZjELRNAUkKqojVitUVPyORuOFTOaKUumIo6OaoiL7bryjoyNmcx3Hjh1iw4af6dNnKEqlkvDw\nGH7++X8ALFjwOYMHj2yydnl5GWvWrGDu3Df59NO30bqe692v02oZ2KsX4x98kH49eqB1c2uY56HT\nUVJWBsCbc+cy9Ykn0Lq64hYRwVGjkeMmE87hdmVlBwcHMpKTGXbrrTw5dizHc3KwWq2X/F62FURR\n5IuFC9m0e3eTgD/A1/c6eiUhISFxaUg7/RISEn8pTp0qYsOG7RQU7EehUBAdHYvFYkEQZOTkHCci\nQsf9999P1sY1aAw1xLUPYe7ve7gLEGX2Yltvb19GjryPefM+YfT9/9fMRn5+DgEB7REEAUEQmDTp\nFd5//302bPiOAQOG0rFjp4Zjjxw5yKJFi/DycuXZZ19FLpdTUVHOypVVqFSuGAw06Znv4eHF7t3b\nm9kcMGAsK1fOo6hoLenpgwEYPfpx3n9/Eo8//hzZ2cc5eHAPZWWneeqpF6mstKea3H77SObN+4Qn\nnriX4cP/hVqtobZWJDc3m8zMbWRlHUUURQ4e3IsgCMSHhhPavj1pSUlEhIa2KCQFEB8Tw+IVK+iR\nloajUomnTgfl5fgEBuLp54coii0qAK/fupX/u+8+Vq1bx619+vyRt/i6IQgCqZ06serkSWQyGR0i\nIkhLSiLQ3/96uyYhISHRKlLQLyEh8Zfg+PHD7Nq1lYKCXIxGe4fEujor9fUWjh49SGJiZw4d2sfE\nie8DENA5naN7fsPq6oZXeSlLC3LpctudcCbILS4u5Oabb2HVqiV4ezfdyf36648aCm3BnsP/j3/8\nH87OZlatWsrq1cvtCrOiSFBQKGPGTG4i+AWgUrni7Nw8sd7X15/8/Jxmr8tkVoYNe5CqqhK2bfuG\nd981oVS6o9XquP/+IVRXV/H2218QF5d03nU5Qm7uCTp1SuHXX1ezfv1P2GyO2Gw1mM115OScoLbW\nQGRkDL1730qX4CBiIr1avd5nFWjnfv01/zhPbEoul2MwGMg5eBA3b2+8/PwAe2qMWqWie1oaU197\n7ZoH/RV6Pd8sW0Z5ZSWyM++rTCbjrttvJ9TF5YLzjCYT5UVFBJzxuzGJsbEYTp0iJT0dt8sUE5OQ\nkJC4ntyQQb8kznVj2JT8aXs2/8r+bN2aSW5uLmDXQFIqHYmJSSI6ujNffDGbXbuO4O7uT3Z2FeAK\nOOIWkQHAiLBufPTRy+iyS3Fzswf4hw+f4NFHX+Khh/ry9ttLKS+HU6dEvvzyLVJSBjaIcjX1R0l6\n+gjS05v72rhpi15v9xGgshIUCvv/z3bKDAyMZfv2A0RExFJSAp6eWro2COR606PHeFxctNSe0uPv\nZeaRu+5n2utTWb1sPj8vX4CDg4LaGiuC3ExocBgP3Pkvjhw/hLHsNCWlJeTkFIC8HqVCSWJ0LOld\nuhPfoRMajROOJfktf8m2cOHv6d2bIePG8dSIETRWKLNYLOSuWUOMUsnJ/fspS01FZ7Px+nff8cjI\nkQgVFahF8Zydq/zBtFgszP7vf5Ebjdw9ciT+3t4NY4baWuavWsXcQ4eY8O9/4+nu3jB2uryc7fv3\nszczE42HB4+PHt1MIdcR6B0aChbL5f8xukaCO23+l/Ov6M/1sCn50/ZstjV/WuGGDPolca4bx6bk\nT9uz+Vf1p3v3NBYtOopWqyMsrAtduyY09NHv3DmZ779/hylTprNq1VckJz/aMG/v3hPk5AgkJg5n\n+/YfsFpXExsbSZcuKTg71yGX2wgMdGbXri0sXbqEYcPuJCmpyxX5Kgjg5NSgWwWAVgvu7vZagfj4\nKD755FWmTp2Nt7cvOp0MT8/mizvKjOjcRV58402mT/g37QMCAHt7TlllJYV1dWzfvZtl//sUm82G\ngwD+Xhr8ndvhHxxMWlISHaOizrXcpA5wuOQ3RafTkZCQwNRPPuHFp55CcWasrrYWN7UaQaPBW60m\nt76e95Yt4/YhQ/CPjgZAVKmaK5tdgs3WxqxWK8/OnMnY0aMJc3VtNs9Jp2PMAw9QU1DAlLlzeXbc\nOKqqq9mWmcnxnJwzF9YRvdXK4fJyOkQ2Vya+HH+u2lhrtOVfzr+qP9fDpuRP27PZ1vyRxLkkJCRu\ndERRJD8/h9LSkhaD7vbtQxg27B7atw+hslJGY92s/v2H8PHHc1CpVNhsNnbu3EpKSlesVis5OaBW\nhyKKIunpKozGGt55ZwIpKd346KM3UCodmTnzWbp0yeCJJ17Fy0vezPYfwWSqAuw7/oJQwfr12zl8\n+AC+vv4EBLSna9eejBlzB1FRKdx222106pSKm5u2yc6zxWLhpTnvcFufPk0C/kPHjrFt40by9fom\nNgVBoENEBF1CQ2nXocMF8/UvBy+djnvuvJOX5szBw9GR0ffcg6dOR563N+U5OczfvRurVsuYgQOJ\n6HSu1sF2ngDY1eLtTz7hgVGjCAsOvugTC2eNhhnPPMOUWbOIiYigsLi4YUzh4ECn+Hh8PD2viY8S\nEhIS1wMp6JeQkGjTWCwWDh/ez65d2ygtPYVMJicqqiPQtA++IAgEB4e1uIZMJiMjoy9ffvkhHTpk\nsGvXVvLzsxk0aAQKhQlRFDEayzh8eBtHjhxi5swP+fLLD3jnna9YvnwBjzwyEblcftGsl8vBzU3L\ngAFgMhk5dCiXxYvf56677mPIkFENgXhFRTlGYyqbNs3njTc+QqX6hvfeex0vL2+Ki0+ybNm31JYU\n8sg/hxMWHIzRaCTzwAF+270bfXU1DYUN2FtuJsfHk9qpk70DT3l5Q+3ClZKWnEx2Xh4vP/00JceO\nMW/JEqprahBFEaVSySPjx9sLXRtdvFqjsSHH/mpiNBqpqa0lMqzlz8H5aNRqbkpJwWqzUVhcjKuz\nM50TE0kODEQtFedKSEj8xZCCfgkJiTbLrl0bOX58G7W1hobXbDYrBw7sJuJMPv6lEh4eTVhYJAUF\neioryykszGPlyiUoFEqMRguiaCE21t7zfsuWdUyYMA1nZxcMhppG6S9XhwMHdvPTT8tQKBRs3ryJ\nQYPuYM+eHWzZsp7evW9teJLh7h7IoEEzcHQ0sWLF6zzyyAi6d++Hh4cXI0bchx91iJTz4y+/sOfg\nwQYl3bN4uLuTlpREpw4dGsS+rja909OZ+tpr9OzWDW8PD8aNGdPqnAXLlzNi0KCr7svC//2vxXXr\n6+vZ+/sRbLZ6OicmNhm7/ZZbeGnOHEYNGUJUWJj9vb5ad3cSEhISbQgp6JeQkGiz6PXlTQJ+b28/\nkpLSiIrqSFXV5a3Vv/8Q/vOfGdx334vccksP9PpKTp8uprpaj8FgQKlUotN54u4ehYeHfRe6oCAX\nf/92V+18rFYrb789nYiIDowf/zzz5n3ClCkf0KlTaMP4mjU/MmvWFO6//zHAHqgrFCp6934UqzWI\n4OBwUlK6kpubxaYNKyk+nd3Mjk6rJSg4mLCOHQkKDLxmAT/Yn6LEx8Sw+tdf6RcX1+rxRadOcTw7\nm/tGjmz12Mslt6CAfw4f3vCz1Wpl/8GDfPLNaopPm1EozPzn5WA8Pc4V7yoUCpQKxYVz9yUkJCT+\nIkhBv4SERJslPj6NvLw9hIfHkJychr9/O0RRRK+vRK8/l6Fyfq57SyiVSjQaJ06dKkSnC8DNTYub\nm7bZcY03eb/99lMefnjCVTkXURR5881pDB06mrCwSERR5OTJfG67LbThGLlcTr9+g+nQoRPvvvsq\nUVGPc0azC5Opip49+zB79vMcPLibsrLTyI3VaOwZPAiCQG1tLaa6OhyVSnINBvL1evTV1dRbLNx+\nyy0kxsZelXM5n5G33847n36KWFXFLRfZwc8rLGTO3LnMfOaZa+LH2c+AKIps3rGD7Zs3U2oyUXTa\nhlLhhM3mwC8bd3DXkH5N5l2N2gYJCQmJto4U9EtISPwplJaWs2PHKUQRUlN98PLSUVdnYseOTCoq\nyujXr3mw6Onpw9ixT+HkdC5/v7KyAvPKxThbNTg6QbWpFv2Aobi7t9755P77x/H885OYNGkKOt3F\nizTnz/+M5OSuuLhcnV7sv/66mi5dMggLs+8o79y5hS5dmqcoGQwG8vJMhId3QSZby0033YGDQxUH\nDuzm+++PcPJkHjqdJyqVCgBXZ2ciQkJY/vPP+Pv44ODggK+3N54KBUYHB8r1emQyGes2b2bNpk08\nNXYsAvaC38rzCn0BtDbbH5JqHzdmDIvmz+fZmTPpnpZGvx49GoLwg0eOsGjRIjQ6HbOee+6aPXk4\nq2osCAL5J09SbTCgcHRE6VCB2tGbsJD29O3eudk88RoVFUtISEi0JaSgX0JC4k9h377TyOUxAGze\nvBUXlwp27tyDg4MZgJSUri0G4o0D/rO4qDQocGloeVl3iT44Ojry2GOv8N57L5KSchN9+w5qphpb\nWJjDJ598SmpqN3r27H/pJ9gK27ZtYNKk6Q0/Z2cfp1u3Xs2O27o1D6s1Gi+vMBYvvg+ZTMvJkwcb\nAlqtVkd1tZ7g4DC6hocRF+PP488/j4uzM/+880571xqwP7I409atxmDg02+/paaqijlz5/Lk8OFU\n6vVUrVyJ65mbB4Aqkwm6dkX3B7vW3NmvH8Pc3dn022+88vbbDTvoIe3bM3nMGBx9fVtZ4coICgzk\n0LFjxJxRyj1y4ABRYWHcPbQTLk6uuLs5oz5T3HyW+vp6LFbrNfVLQkJCoi1wQwb9kjjXjWFT8qft\n2bye/tTViRgMVvbtW0ZJyToCAz0wGOz96gH27j1Op06eLc5tjF4Pzgag3v6zwQA1jYStWvOnpsaJ\nRx99nQMHdjBjxkvI5XJUKg0WSz1GYy3Ozu0ZNeppnJxcmn3X/NFrkHvwMH5uWoSKcwvaqvWIFWXk\nb9uHXivg3zEBR6Uj5vJKiku2cyJnByUn89ixYSV+fvbgXSbIaOflQ9fUdHp264VjST6vvfkmznI5\nc8aNs9/AtCB45Qw8PnQoi3/5hZ82b+borl14hofjarXS5PmI1QqnT4Ob2x870ZISBCAjIoKMiIjm\n8y60w/8HPpjm+nr2HjlCdWEhvW65BYAR6em89tlnPP/IIwQ7OTGuXz904eHnJhmN9v8arbls9Wpu\nT0ujBaW1y/Lnmo61xl/ty+JG8Od62JT8aXs225o/rXBDBv2SONeNY1Pyp+3ZvF7+9O4dyt69J6is\nrMbZWYtcDnK5A8nJ8SQmdsHLy+eS1hUEcHQCBXZxq3rA4Yyw1eX40717Kt27pwJQV1eHQqFAJpM1\n3iC/AKXs3LmFmppqnJ1dSEm5qeEJxYXmZdUeJD0pHH/duWcSMSE6sneuYJiTE0K9C/sP/EKNqyv5\nuZs5klWGTAaxkX7U1RTh7e5DclwcqQkJzFuyhN5dIvDV1VFfL7Jx3z5+/vZbHBoLE1zgIgwdMYI9\nubl8tWUL41NSmiuEAXh5tb0PbaNxfVUVv+3Zw659+zDV1SGvqyNVqcTF2RkV4OzpydGKCiLDwtAJ\nwkXXNahUbD1yhDvvuusP+/OnjbXGX+nL4kbx53rYlPxpezbbmj9XS5xrzZo1TJw4kczMzCav//jj\nj3z44Yfk5ubi5+fHPffcw+jRoxvGzWYzs2fPZsWKFdTW1pKens6UKVPwbiSNLiEhcWNy6lQZ69aV\n4OICycle+PraA2CbzdakuFapVJKaGkl0tC/ffvsp8fHJBAYmExjodNk2q021yKz2gL/aVMuVZog7\nthQwn8f+/ZksXrwEPz8d3br1wsXFlaoqPQsXfoleX8FNN91BenpSi3Mtlnoc5Jomr/VOT+fJp56i\nV0oK27OzWVNYiFv79ggyiA6338GY653RBQYw7sEHG/LgC4qK8D3z3fnNjz+S2qkTjo6OmM1m1m7e\nTFFJCWaTCVe9nuSbbiIsMbFJy9Exo0bx2NNPU19fb1fQvUEQRZGlq1ax//DhhlQnAKvNxpETJ0g5\nI/z1+AMP8MzMmTx0992EX+SJRU1tLVPeeovnHn/8mvsuISEh0Ra45KA/MzOTiRMnNnt9xYoVTJgw\ngTFjxpCRkcGWLVuYPn06zs7ODBkyBIAXXniBtWvX8swzz6BWq3nzzTd56KGHWLx4casdNyQkJNo2\nBw+WolTGoFDA778fAcxkZm6jsrKcUaPGAE07o7i4uPLgg08gCMIfaofu5qZFP2AolZX2HX7lmdeu\nJYsXf4PJZOTf/36xmSJvbGwCFouFzz77nNOnD3PHHf9oNt/b05vC4uMNP4uiSF5hIScNBp5dswZH\njQaNn1/DeHhwMGlJSRzLzkYLDQH/b7t3k5qQ0HDckrVreemZZ5j13nvUGo30v/lm4mNi2LZkCWW/\n/86bmzYRkJDApAkTGtYICgzEJoqU6/WoTaYmflaZTFxO2bLJZCL/5ElMdXV46nT4Ka7dbYQgCDjI\n5Q0Bv0wmIzYqii4hIQTExDQcJ5fLmTF5Mm989BGC0cjo0aPxb1RLUGMwNR8MZQAAIABJREFUMH/Z\nMo4fOsSU8ePxvJLddQkJCYkbiFaDfrPZzBdffME777yDRqOhvr6+YUwURV577TXuvvtuJkywt7Xr\n0qULhYWFbNmyhSFDhpCXl8eyZct44403GDBgAADR0dH079+fNWvW0Ldv32t0ahISEn8Gjo4i9fVm\n8vMPc/LkD+zefe47orAwD40mqNmcK2mRKJPJcHfXIYrNU3quBatWLUWhUDJ06N0XvElxcHDgzjsf\nZNOm7/jpp2XccsvtTcajw6NYuW4pg/v1Y9+hQ2zLzKS0vJzw0FA2bdlCn549cVQq6dShA12SkvDy\n8ADgi4ULmXb//QBU6vV8u3Qpr0+d2rBuWWUl//3uO1548km70i52BWNtcTFD/fwY7u/PKm9vnp4+\nnZeffhqXM6k8SgcHnDUanM58J5/FFdBeQlHrsawsFixfjiiKRIaGolKp2LprF7nHjxMTF8edt97a\n0F3ojyCKYoufkS5JSRw6fpyU+HhSExLs59PCm+Lg4MCkRx+lIjubeatWUVZRgfzMBpMgCNx1++08\n0L//laXTSEhISNxgtBr0b9iwgY8//phJkyZRUVHBZ5991jB24MABiouLGTFiRJM5s2fPbvj3tm3b\nAOjZs2fDa0FBQYSHh7Nx40Yp6JeQuMHp3DmMNWvepKqqAG9vLWDfCXd0VKHXV7YY9N8oWCwW9u3b\nxb///TSbN6+jqKgKX18XOnVKabGV5+DBI3jttan06XNbk5Sa6ppqao1Gnn/99SbBsEat5ubUVI5m\nZfHWtGkNaTsAp06fxsvDA5lMRm5BAXPmzmXahAkN3YZKy8spKSvj4XvuaQj4wS5IpW3XjoL8fEwa\nDQkpKfQbNowXZs/m9alTkcvl1BiNeHl4tByYt/L45fMlS6iVy3ly7Fg053XCobyc30tLmTxjBhMe\nfphAf/+LrnU+JaWlbMvMpNZo5K7bb2827u3pyVNjx16yQrK7mxuP3ntvy4OS6q6EhMTfjFaD/ri4\nONauXYuzszP/+c9/mowdOXIEsP9hHD16NHv27MHT05OxY8cyatQoALKzs/Hy8mr2x6Vdu3ZkZzdX\nkpSQkLj+1NXVsXNnNhYLJCUF4uLSvG3mWRQKBRkZPcjMXAWAu7sHiYld6NgxAaVSeUPHVl9//RGn\nTxfz+efv0qVLBoGBocjlVXzxxfvU1ZkYNGgE4eHRTcTCIiJieOGFJ4iJicNiseLg4EBZzlHUajVr\nN22iT0YGDg4OBPr5kZaURIyHB+UyGZ/Nn4/RZGJAz54E+Pry0pw5DOjViynvvIN/cDCznnuuSe3B\nJ/PmMbRPH7774Qemjh/f8LqjoyMe3bpRu38/Vl9fImJj7U8ibr2VFWvWMLB3b0x1dX9oJ/7LhQvx\n8fBg4JnUzZboEBnJrOee45mZM3lu3Dg8WtlNF0WRYzk5bFuzhqy8vIbXT5eVNTzxaMylBvwSEhIS\nEk1pNej38Wm5owZAeXk5crmcRx55hLvvvpvHHnuMn3/+mWnTpuHm5sbAgQMxGAxoNJpmczUaDcXF\nxVfmvYSExDVh164cqqsjkclkbN9+iD59YhBFkaqqKlxdmxdHxsQkotdnEx+fQkhI+F9C4XTlyiWs\nXv0Dn322tCFAPtvZR6n0paDAxIIFC4iMDOFmq4VjJ/L5LWsvHi4uFB4/jIeHF6WlJeTmnsCoL6dj\nZCjpnTuz5+BBXn32WWKjozm7qJdOx6RHH8VsNrPsp5+Y+vrr9MnIwMPdnZcfewzhvODXbDZjqqvj\nviFDmPz++9TV1TW5IQiKiQEfnybpK11TUpgyaxa1RiNx57fTvARKy8spKinhn2c2dC6Go6MjL02c\nyOwPP+TFp5664HGiKPLZ/PnknzgBjZ4aKBWKhicdEhISEhJXhytq2WmxWLBarYwcOZKHHnoIsOf0\nFxQU8N577zFw4MAL5mYCf7iIV+rTf2PYlPxpezYv1Z/KShGjUUAQwGq1sHHjLjZv3oZKZWb06Meb\n/e5WVjrSvbs9GKyouPr+/FljAMaSKn7dspqa0hI6xyWhqjVAreHMZCtl5Xqy98vRaNoRF/Ivigu/\n4e1t6+noFcKAmDj25GWhr67CUFaKTq1BFx2HxmzCbKsh1NubKf/8Jx8tXMiiRYsY1KMHkU5OCLW1\nnMjPZ9natTg4ODBv+nQ8zxYslJTYHyE04pcNGxiQnEyUkxMBWi0vzZjB9HHjKDqtZ8eBGhzkIj2C\nbDR+PiMAToLA+59+yjcTJ144veUCF+jrr77ivoEDWxzPKypi0erV1JaVITg7E+jjw/B+/XC0WqnK\ny8PV2bnFeQIQotWSb7BfX62LC53j4kiKiUHl6Nii5sCl+nvNxq6Xzbbmj/SeSNegrflzPWy2NX9a\n4YqC/rM7+BkZTaXku3btysaNG6mvr8fZ2RnDmS/0xhgMBlxcXP6QXalP/41jU/Kn7dm8FH9uvrk9\n69bt5PjxA5jNeeTliVgsdu2m8vLDREZ2+NP8kckqWLZsPpWV5YiiiFqt4bbbhuPt3e6a2FTYqliy\n5FdmPPMM0996q0lvfX/KqZDJ0TqZcXGqRykYqXSo5XBRDqFuKnbllNrXkIu4qCy0D9SSlpREvI8P\nCh8flqxcybpDh5g0cSJms5kVa9eyafduRLWadv7+PP3UUy2n3Zzn8MnaWpJuugkcHPjn6NEsXrGC\nKR9/THzMYDSuqVhFkR0lO+iZdG7e70eP8svOncTHxRHYseNlX6Cyujq8IyKaKP3uOXiQuZ9+io+L\nC/fedx9Brq6g03EsK4s3Fy6k0mTi89WrGTdmDEajEU0L66ZmZJBXVETn9HSiw8MvvBnUFn9R/myb\nbc0f6T2RrkFb8+d62Gxr/lytPv3nExRkL9Br3NEH7E8ARFFELpcTHBxMaWkpZrO5oWUcQEFBAamp\nqVdiXkJC4hqh0Wiort6PxZJH4xjM1zcApbL1nvYXQ6+vZMmSb6ioKMdkElCpRFxcXBk6dDQ63bl0\njpKSYj766AO8vFwYMuQf+Prai0KrqvQsWTKP7OxCRo0aRVRUxyvy53zWb91A/zONB8z19c2eVrpr\n3YiPKWDHnjUUnjrAijU/E+btzZFTp4gIDEQURVzVakYMGkRSfLx97pkd6zsGDGDqa68xuF8/lEol\nQ/r3h86dLzvYkzdqXdk5MZEKvZ4tO3bwwy/zEcU1JHXsSbhXOXmFhew5+P/snXdglFX2v5+ZySSZ\nmfTeeyAJIQkTIKEKUhSVblnEVX9iXyy4KnZd18auddXV1XW/uta1YceKigoBQgrppBLSSJlkkkwm\nk2m/PyYMmSSQ0CTAff7R3Pvee857Mxk+733PPaeI7Tk5mMxm/Hx8uHZADZXRYjabcXVxQdPezoHD\nCz9nZZGdnc3fzj4bN2dnSnbvxjJ9OlIgPiaG+269lTaNhrMvvti2ASSRcN111w158+vu5saVy5aJ\nTDoCgUBwgjkm0T9lyhRcXFzYtGkTavXBojQ//fQTKSkpSKVSpk2bhtls5ocffrCn7KypqaGiooKb\nb7752LwXCAQnjNTUKdTX1yKVSomLSyQ2NpPExLCjjtfv6+vjX/96CplMxvLlqwkODrVvGjc3N/HB\nB2/Q09PN9dffQXNzI//970usWfMwISGOGWI8PDy54oobaG218N57f0OrbWfq1JnH45YB2Jm/iw33\n3gbAWZmZ/Pjbb5w9cybVhYX0lJZSLpGwT6ulrb2dorIyxsXE4NneTk5VFYsnT6bXaCQhOJjoiIhh\n1+qC+fP58ocfWLJw4VH7GBsZSdGePYQkJwNwzpw5RISG8sb7H9Ci0VC//xssOiuVbXvQ6fVYgSmp\nqfj5+BARGgom0xHZs1gsGPr6+HhTH0qzG3WdtezML2T95XNx7eoCQG61YrVaAdD19JCdn8/O/Hy8\nPDz48MsvWTh1Knvr6ogKDz/q+xYIBALB0XNMot/NzY3rrruOF154ATc3N6ZMmcJXX31FdnY2r7zy\nCgARERGce+653H///XR3d+Pu7s7TTz9NQkIC8+fPPy43IRAIjg69Xk9LSxMREdFD+saNS6K9vY3k\n5El4eHii0QwJLR81fX19PPbYXVxzzTpCQ4eKvoCAIK677jaam5t4+OE/I5M58dBDT9PZeeivKKlU\nytq1d/Hss48QFBQ67D0cDU4ymV2snz1zJnc//jixERHs/Pxzqvbupc1kQhoZiUKppKWtjWnp6STG\nxeG8eTMLb7qJvzzzDKtnzaIlKwtNUBBxkyY5zD910iT+8vTTxyT6Z2VkcN+GDSzoF/0AifHxPHHv\nPbR3dPDdli1oGhpQ+viwcOJEkhMSMJvNPPTUU3h6eBxxukq5XE5vby9KV3fckZOz9U2WLryesHF6\n9hQV4dTbiyIpCZlMhtVq5Y3336e5rY3e/gPG42JjaW5rc6ikKxAIBILflyMS/RKJZMjO1Y033oi7\nuztvvfUWr732GtHR0Tz//PMOcf6PP/44jz/+OE8++SQWi4Xp06dz3333nRYZPgSCU5G2thZycrZT\nXJyPTCbj2mtvcwi/A1sIyfTpc46LvZdffvKQgn8gAQFBREbGUl+/156PfiSuv/52Xnrpb6xb98Dx\ncNX+vWS1WqlraEDh6sp1d91FslKJG2Dt7w8PCWFiYiK3XnMNUqmUXTt28L/PPmP+jBnEarX4KpW0\n1NSgiYjAZ8B33YHKssfqY3BgIDX19UQNCovx9vLi4iVLHGLvAT795hvOO/vso7Ypk8no6+uls68L\nldITqVSGs1xO0oAaLJbWViRAekoKm378kcLSUpYsWMD58+fz6muvERN56tZsEAgEglOdIxL9a9eu\nZe3atUPa//jHP/LHP/7xkOMUCgUPP/wwDz/88JF7KBAIjhv79lXy44/bqKmpsLeZTEaKivKYNGnq\nCbGp1WpwdnYeUfCDTWi3t7cREzOOpqYGnJ0PFncy91eKHZyn3dXVFalURk+PDqVSdcz+9hoM5BcV\nsSMvj/r+tMJ+vr5sq6oiJTCQiVOnsnTpUkKDg3n0ueeQSqVoOzvZvGMH62+5hYzUVPQ//wxAH+Am\nlw8JpzkeGx7XXHopd95/P3+5916H4lzDUVJeTmFZGSvOO++o7S0791z+9dbHyM2QkjwbbXcbjc09\n+PRnGaotK8OQnY3R05PoKVPIVKvRdnaytr+icKCvLx1a7Yi+CgQCgeDEcHQ5MwUCwSlJQcEOB8Hv\n5CQnJWUykZExJ8zmF1+8zfLlq4e0m0wmOjraHBIB7N1bRXx8IsuXr2bjxrft7U1NrXzxRTlffllN\nZWXdkLnOPnsRv/66+Zj81Ot7yMraQqumlWdefdUu+AFSEhNZe801BEZH06zTkVtYyM/btlFQWsqD\nTz7JnY8+ypVLlnDOnDl4eXtjSk6mXKHAmpJiC6cZgNlsxjhCTH1LWxtbsrL49uef2VVUZH/gGYiL\niwsP/+lP/OXpp9mZlzfsPGazmU++/pr3P/+ce4/xDNWUtDT8fZqQSArx8y2koeldvv7xR7tvvRUV\nxCsUJDk701FZSUFpKf/vkkvs4wN8fNB0dByTDwKBQCA4eo4ppl8gEJxapKRk8t13Zbi5eTBp0lQm\nTkxHoVCMPPAY6OrqIDAwGLAdCNVqOzCZjOz76Ws82vuoDvIk8pyluLi40N7ehr9/IF5e3hgMB1Nl\n7tmjQaGwFbMqL99DbKyjDV/fAIqLdx+Vf62tzeTkZFFcvBuz2USAXwDbdv1KVHg4Ab6+ZKanMzEh\nAblcDrNnY1CpKC4vp6u7m7nTpxMZFsaWrCwuW7zYPmd4XBzExQ1r75uffmLB7NnD9m3duZOvNm/G\nz8cH9cSJeHl40FBdzSdPPYWvtzeXX3ihfWcdwNPdnb/ffz+ff/cdn3z9NeEhIYSHhGC2WKgoKqLD\nZOKcOXN4YN26Y367UFtfT0pSEg9s2EAvEBUeTpdOR2lFBRPGj8fo7IxZr0fb08O/f/mF8xYtIiXp\nYGrXXoMBl0EhZAKBQCD4/TglRb8oznVq2BT+nBybRUV1WK2NJCcPTYkrl0cxZ84qIiLikMlk6PWg\n159Yf3p6JPa/Wa22A+v3HyM1mQitKKBbL8VZncbeimb8gsPp7XWltbUXjQZ6eyX2ea1WKe3tPUil\ncpRKMxqNrYgW2N4SGJobcTUZQdPW75AZOETcfLMZq1VK1d5KcnbvorauxqFb0tPD7NRUFBYLNyxe\nbBPL/RlqaG7GJSCASSG2sKNZ8fFc8Kc/cfPq1UhbW+FQOeb7b6TXYOB/H3zAVcuXs3nTJnJLS9G3\ntYFKxa+5ucSFh7Nh3TpUA6uYKxQsnjOH5rY2nnjqKa5cupSkA089zc04AcszMliekUFjSwtNra3I\npFJmzZyJ94HrjkPFtB+++YZ9TU3MSkqivLyc6qoqpqWmYtZo0FRXY3Jz466NGzG6unLD6tWMj452\n+LKurawkQCoVX+BHa3Os+SN+J2INxpo/J8PmWPNnBE5J0S+Kc506NoU/v49Ns9lMeXkJOTlZVFXV\noVLJUKsTcHMbXABPgo/P+BPuz0BUqoN9Egm4+Cpxd3WlTeOGrENPgxLiY/xQKCAlJYbs7C/x8VmC\nq6uVgADb2NmzY9mzZy99fWYSE+NxcgIXuvHtn3fzj1nMmZpiL6QVgmZYh/r6+qgr2MaPWytp6xfC\n7v0vOpzlctImTCAjOhrfmBg++/ZbnvnwQ264/HLHtyH98/b29vLym28yc8YMyltbOWfGjEMuQm1j\nI2+//TY/b9vG3BkzeP3rr5FJpXh7eeHr4YHByYmH1q/Hy9OTpz/4AE8PD9b+v/93sFCVjw8BPj48\n8fDD3LdhA9eHhNhSbw5a+GAfH4LH9/9+NcOvwYi/sEP0Zc6axb4vviBpwgRMKhUvb9hAWWUlu0pK\n+LWsDF9vb+5bvx7PYQ7rdnZ14eLlhTww8Oj8OQp/T2jfybI51vwRvxOxBmPNn5Nhc6z5c6KKcwkE\ngpPPrl1ZZGdvpbu7095msZgpKMhh2rSzTqJnNlxdlWg0bQ6Ft5yc5PikTaW8og7/uefaRbVK5YbR\n2Ed7e5tDZVaJRML48VGHtFFSXs5lK1cesr9Dq2VHXh45BQX0dnTAABHv5eHB1LQ01BMn2qrh9u9E\nL1m4kPKqKp58+WWkUinTJ0/Gw2KhSyZja3Y2ZrOZS5YsYVxsLD/88gsPv/QSa2+80SH8BuDrH3/k\n1y1bMMnl3HL11Xz900/8/f778ev/0i7NzuaO558nU60mMz2d+9eto7C0lAf+/ncevuMOh4NXUqmU\nh/78Zx557jkevuOOEdf+SGlqbqa1uprkjIwhfYnx8axYtIgkPz/+8+237GtoID0lhfSUlIMXHSIV\n6GvvvsuqYzhELBAIBIJjR4h+geAUp62t2UHwe3v7M2tWJomJKYcZ9ftx/vmXsnHj26xZYztI2tXb\nY++TKFW4uioGXX8hDz64jttv/8th57VYLGjatWTl5BAfE2OrFgt4eXoixZYJaF9DA1m7dlFSUWEv\nHHWAiNBQMtVqEuLiHB4wBhIfE8P969ah6+kht7CQ5sZG3IODueXqqx1CcObNmkVSQAD/+d//6Ozq\nYmJCAu5ubmTl5LAjL4/5ajWrL7uMZ//9bx5ct87hweCzn37izX/8g3c2buTXHTuYOXUqyQkJXGK1\n8s833mDt0qUOPjk7O+Pl4UGrRoPfYVdodFgsFvZUVZG1axc1dXW4mM3Ep6Xh4uJYeVkqldpi9DUa\n1qxaxV2PPcYta9YQFhJyiJltfP7tt/j5+BA5wnUCgUAgOLEI0S8QnOKo1ZkUFuYSFRWHWp2Ju3sM\nvr5jpwaGn18Q7e1taDSteHn5oF20ggNHdCUd4Onp5XB9cHAYbW0t1NRUkpx86IJb2i4t735SSm7h\ndlaedwtf/yihp7eLpQs1NJaXk1VZScP+/Q5jZFIpE8aNI3P2bEKCgrBYLHRotYMm1uLl5eXwIKBS\nKpk5dephQ2aC/f25/frrMZlMVNTU0NnVRX1jI5+//joyrZZSjYbEuLghbwK6e3rw8vTkxiuvZP2j\njzJjyhSsVisu3d3sycmhNi2NiEE2L1u5kvc+/XTIA8GRsiM3l227dtE+YA0MfX3kFRWRMaDK+mBk\nMhmP3HknDz/zDCmJiSxftGhInYfm1lb+8957hIeE8McLLzzigmACgUAgOL4I0S8QjHH6+vooLs6n\npWU/CxZcMKTfzy+Aq6++FQ8PW/7zsaitbrzxTp544h5uvfUBhzAfq9Xx7Gtnp5Ynn3yAf/7zXTZt\n+pidO//OFVdcgZ9fgMN8Ol0373/2IYWlnVxx0YNIpVJ6DTr2VpTw0n9zsPT0OITwKBUKJqekMCUt\nDfe+Prtw79Bq6dy0CQ9X14M+tLXBoCw5R4KTkxMJcXF8v2ULq5Yts9cV+ODzz7n9+usdrjWbzTjL\n5XRotbi7uTF/1ix+2rqVKB8fIpqbuXbSJN5/+23WpaQ41CcI8PND29nJsVJVW+sg+L09PclQq0mb\nMGHEsS4uLjx6113kFxXx2PPPA+AulWJydqZbp8PX25s1q1bh7+s7wkwCgUAg+D0Qol8gGKN0dWkp\nLNzB7t27MBh6AZg0aeoQAQzYBf9YRalUsX79o7zwwhOEhISzbNkq3N0P5q/v6dHx6afvUVNTwZ//\n/Bc8Pb34wx+uoqKihY0b36ajQ4OzswtSqRSDoRdnZxfOmZxJdHgs2q5Wyiqz2VtXgr63m7goEwfk\nfqCfnz3lpr3C76CnIg9XV3zc3A426HTH5Z5/2bGDh/78Z4c2hUJBUVk99Y0mwkKciAj2oK2yEuPm\nzZS4uTF37lz++uyzXLFoEVKJhOSwMF795Zdh5x8crnQozGYzUql02JSdmWo1pRUVRIWFkZmezriY\nGKQdHTAotOdwpE6YQGr/Q4K+oQEnf39belOBQCAQjCmE6BcIxiDff/8l27fvwtXV4tBeWVk2rOg/\nFVCp3Fi//hHq6vbyxhv/xGg00tsrwdXVilQqZenSP7Bq1RqHMT4+/lxzza2ALfbcarUik8mwWq0U\nbfmSLds/RtvV6jBGIpEwPjKSzNmziQoPPy7Vb48GuZOTg22JRIKmvYP8YiXuqiDyipowd1egMJnw\nV6mw6HT06PU4y+VEJiRQ0dmJRKfDNShoSBXiVo0G94EPKoegqriYws2b0fT2snzNGjwHvb2IDAvj\nT1deedx24xWuriAEv0AgEIxJhOgXCMYgrq4KrFab4JdKpYwbN4FJkzIICQk7yZ4dO2Fhkaxdexcw\nclbJgdh2+Q3k52eTm7udlupS6pvMgE1Yy2RyAv0jWbMqk3gvr1FP3NnbO+Rnj0Nce6yYLRakEpso\nlkqccPfxxSiTsV+rpVWhYHx/SJJMJmP8tGkAuO/cOWSetz/+mD8cJp6/W6dj544dbPrqK5QmE1KJ\nhMqcHNTz5jlcJ5FIRPiNQCAQnCGckqJf1HY5NWwKf0a2uX+/lQOidSCRkVOwWHJITJxEcvIU3Nxs\nMvTAZ/90WYOBBbaGH2wrstXW3sYnX31EQXE+FqsVX29fPKxmogO6cWlqYmpkJBOCfekzteBttY7a\nIS+LBfrF9QE8WlrwMpuP/ItmUJ+xqwtrW5ttt7+5mb7OTrzNJmK869jfVkucv4Uoj1CuuugiXtu1\niz+vWYNMq6Wvs9Nue29DA35OTg6+mEwmWuvrCZTJhvXni59/JrekBHNXFzq9HleLhT6LhZq6OtTH\n+gE6lrGnks2x5s9InAlrMNb8ORk2hT9jz+ZY82cETknRL4pznTo2hT/D97W2NpOTk8Xevc1cffWa\nISEoPj7u3Hjjbfj5HaLC6wny9UTNe6i+gQW2BmO1Wmkpy+Lul96jZt8+QoOD8XJXYTaZ2FdXhtRk\n4pwFC1iVkYGfR/9DUXe3zZjVOiqHpICP36DEl56ex+VG1VOnsrO2lqmTJgFwweLFfJmby9JzznEY\nkjh1Kl/V1FDU0oLBYGBKZqZ9nnffeYcbL7nE/rPVauWvTz3FlVdccdDWIH+sKhVmFxewWPD18cHX\nxYUpkyYxddo0NAcqC7u42Cql0Z/idHDK0rH0ITlZNseaPyNxJqzBWPPnZNgU/ow9m2PNH1GcSyA4\n+VitVqqqysnJyaK2tgoAvR7q6vYSHh415PpD5Y4/3TGZTBSWlvLpt9/yzfffMz0zk+iICMCWcjM5\nIYFMtZogJyfe3bKF5775hoeXLz9psfuH4vx58/jL00/bRf+UtDRuf/hhzp0zZ0gO/Nuuu44nXniB\n/KIi3nrhBQAampqwWCx49Mfud2i1/P2ll7jwgguIjYrCarUO844IMtPTKSwrQz1uHFNnzcLby5YS\nVdPefjBTkU4HKpUttGnRoqPOVCQQCASCUwch+gWC34mNG9+hurrcoc3JSU57e9uwov9Mo1unIzs/\nn+zdu2loamJ7bi4Lpk1DolSiUiqZkprK5NRU3FQq2wCNhnPnzMGrooKHP/2Um+bPP6Hx+EeKk5MT\nk1NT+fCLL7hw+nQkEgm3XXst927YwKPr1zsIf4lEQmxkJCaTiQeffBIPd3fyi4u5/brr2Lx9Oz8X\nFaFSKrl5zRrkTk5s2ryZzq4uLpk1a4hdf19fbr/+euRdXeDlWAPBIVPRKA4CCwQCgeD0QYh+geB3\nIioqzi763d09mTQpg9DQSYSEKEYYeXrT1NxMVk4OBSUlmC22w8u7CgqYO306QR4eTJs1i+SBKTcH\n4OXpSebVV7Pnww+pjo0lOiICL09P6Oj4vW9jWJaecw5vf/wxr374IVetWUNocLBd+GdMmsSyc89F\nYjLxn7fewk2l4parr+adjRsprajgkiVLqG1owNfJiXtvvpn6piY2//Ybe6qq7Ok6m5OSCBjmNa9I\nmSkQCASCwQjRLxAcR6xWKzpdN25u7kP6JkxIo7q6nIkT1cTFJSCVSsdkIa3fA4vFQlXVHnZv2URr\ne51Dn8FgIDQoiKsvvZRIpRLJYbLLSKVSfLy9uWb1ap599VXuveXYpwUJAAAgAElEQVSWE+36EbN6\nxQpyfv2Vvzz9NF4eHpw9YwY3XnEF23NzOf/yyzHq9SQmJhIcEMBL//0vq5Yt4/rLLz84gUbDfz/9\nlKraWod5nWQyGlpaCIiPH7Uv9kxF/bUIxtKbEYFAIBCcWIToFwiOA2azmbKyInJystDpurn66luG\n5FZ3cXFh5crLTpKHYwODwUBhYS65udvRatuR6btQ9r/ocHF2ZlJyMiXl5fzpyisJCwlxyFrT0tBA\ne1kZVldX4qZOZeDqqpRK+oxGW5z7GIvtB1AnJaGeOZNWjYZt2dl06XS4q1S8++KL+MKIB8HCQ0Ls\not9NpbKHOqkGpRs9HF6enrBoke2Hjg7w8sLjQLtAIBAITnuE6BcIjoGeHh07d2ZTXb0Tna7b3l5e\nXkJCQvJJ9Gxs0d6uIS9vB4WFufT1GRz6vD09mZqWhnriRFxcXMjOz7cJ/gFYrVbasrNJcHWlT6+n\npqiI2DDHmgWe7u706PWolMoTfj9Hi5+PD4sXLnRsHPBg09vbi6ur65Bxk1NTqdy7lympqSQnJBx8\noDwC0X/grQhgy24kDu8KBALBGYUQ/QLBMfD55x9QXl6DYkBYvq9vAHK588lzaoxgtVqpq9vLli1Z\n7N9fZo9DP0BYWBTTYmOYqo458kxFg+YCxuQO/2iwWCyU7tlDVk4OBoOB6y+/fMi9uLu5cfWll54k\nDwUCgUBwOnBKin5RnOvUsHkm+BMdPYW8vBokEgmRkeNISckkNDQKiUSCRnN6rcFhC2n1F9ECW8rN\n0ooScnZn09LajF5nRaGyiVipVEZifBLq1HQC/AJxad6HdNChW39XV+pLSggNDLQ7JAF84uLYU16O\n1dWV2ODgIc527N+PUq+37X6PYhEOPIQMeVj4nRa+12Agp6SE7Vu3oh3QXlNQQPTAtxin0ofkdLE5\n1vwZiTNhDcaaPyfDpvBn7Nkca/6MwCkp+kVxrlPH5ungj8FgwGRqwscnckjf5MmJtLbOIjMzDR+f\n4Q+cng5rAIcvpBWChu7+0Jyd+fnoenoAcFeAO3pUvr5DU25iAJyGGLz4D3/g3+++yx033ODgUICP\nDwFJSfbrGtvbeet//6O3txej0cjWwkKe+fBDLlu5koCAgGFvpGrvXt779FP6nJyQ92cDMvT1EeDn\nx+rly+057X+PhX/jzTdpPPDl3f+qyMfLC6NKNXSeU+VDcjrZHGv+jMSZsAZjzZ+TYVP4M/ZsjjV/\nRHEugeDI6ehoJy9vBwUFOej1EB9/25CiSlKplMzMecf0b/VYxWKxsGXLd+TkZCGVypD3duHh7sTF\nixfbi2UBNO7fz/YtWyioq7On3DxAkL8/mXFxJE+dOmzKzeHw9vKiW6fDYDDgMky/rqeHv/3zn/i7\nurJm9Wp8vL359zvvcMXFF+Pq4sK/3noLvUbDfXfdhbI/vt9sNvPECy8Q4OfHzatX4zboPEDj/v28\n/OabRISGsnrOnCNap6NlUnIyjZs3AxATEUGmWk18TMwpG6YkEAgEgrGNEP0CwSDq62vJzt5GZWWp\nPQSkrw+KivJQqzNOsne/D1999TEFBTmcddZC1q17AIlEgoumARcXHe9+8gmVe/dyzllnUVNXx966\nOltp4f7daolEQkJsLJnp6USEhiJpb4dRCv4DXHPppTz09NM8es01DIz21/X0cM8TT3D/rbfiB+Dt\nTX5RETvz8qjZtw+lQkGgnx8tvb3Mvegilp57LldceCEvv/kml61Ywfi4uGHjA4MDA7n7ppv44rvv\n+O9nn3H5lVce9doNxGg00tbeTtAw9582YQItbW2kh4fjHBAAQPuAUCcvT88zoipz0759dJWX45+W\nhtfp+PQsEAgEYwQh+gWCQWRnb6WiotT+s1QqY/z45DOmau477/wbP78A1q9/ZEifk0xG8vjxaLu6\nuO9vfyN94kR7RhgXZ2fUEycyNS3tYJjMURIWEsLlF17IXc8+yx3r1uHfn6v/iRdesAl+Hx+sbW1s\n/OornvrXv7j/1ltZeNZZB0WyRsP/u/JKXvi//+PORx8lPirKJvhH4IIFC3j++eepqK4mLjp61P5a\nLBY6tFro36Xv7O4mt7CQPVVVOMlk3LpyJbJBY5ydnTl//nw01dV0btqEx4CsPZ29vbBo0cFsO6cp\nmtZWLLt2EW+xsGfLFpTnnYezszgELxAIBCcCIfoFgkGo1ZlUVJSiVKpISZlMWtoUDAa30zKEZzBb\nt36Hh4cXCxcucWhvb9dQ+MtmKvcW0me0HeadN3Mm3/78MxcvXsyM9HTSpk0bEv50LCTGx7P+qqt4\n48MPaddqGR8bi8lkorKmhg+/+ILq8nL2NDTw+euvDyuOQ4KC8PPxQW8wMDk1lf9+8AGXX3TRiHav\nWr6cZz/66IgKfXVotXR+/z3dMhnZNTWUNTaiNxpxiYtDoVBQXFnJRH//Q473cHXFx81t1PZOF3Ra\nLX5OTtDXh7vVSq/BIES/QCAQnCCE6BeckTQ3N9HUVE9KSvqQvrCwSBYvvpiYmHH2OHSDYchlpyXZ\n2T/x0EOPArbsNvv21ZCTk0VV1R6kPZ32QloAsZGR3HPTTeh6eshISYHjKPgP4OvlxW3XXYfZbObm\nBx5g9tSpdHZ3c8H8+Xyg0XDTDTegUippaW7G08triGBMmzCBgtJSLliwgKdefpn6xkZCXVwwGo3U\nlpYiVygIj411iKNXKZVYLJZD5sw/FB6urmwuK6NWo8FFLsfSXyhswrhx+I1ix76lsRFjRwcuvr5I\nxnCtgeNJcGQkZTU1uGq1GGNjCXYfWslaIBAIBMcHIfoFZwwWi4WKij3s2pVFXV0NUqmUmJhxuLk5\nCg2JRMK4cUmHmOX0paKilKio8baUm6UF7NqVRWvrfgAMhl6cTSacZC5MTEwkU60msH/n+r4NG+Cs\ns06obzKZjCA/Py5ZuhSwpQVt7+zE39eXym+/JcRsptzJifiFCx2Ef219Pb79gvuqA1mBLrmEim3b\niOvuxmCxUN3TQ0xKioO9cTEx1NbXMy429oj8zIyNpVajwVUuZ2JYGOrVq4mOjBw+z/AAurq7cd+7\nF39XV1qqqjDEx3MmyH4nJycmzJuHpbUVqZ/fyXZHIBAITmuE6BecEeTnZ/Pzz79hNLbb2ywWC/n5\n2cyYMfckejZ2+OabTwkKSuLVV5+hp0dnb2+vr8XXYGB8YAALly8nItIxdam3pyfdPT24neD4p4G7\n8Zs2b+a8WbNob20l3GLBXanEqtPR1NREa00N323ZQp+bGzvz8uju7qaypobYqCi6uruxWCzIdDrk\nTk7IgYbOziG2FAoF+kNUu23VaGhubSVp3LghfQnBwSxJSyM5LIzu3l7w8BjVvWl7epD0v07q7u2l\nR6c7I0T/Ac6EA8sCgUBwsjklRb8oznVq2BxL/lRVNdHY2M6BFPEeHt4kJ08lJmaS/fN0uqzBaIto\nHaCpuZGc3bv4+YcvmTiuBaXbQQEW4BfIpJAIFkTGoOhqZ/+ePTAoBMNbLqezpga3Q4WkHK8F0uvt\nf/zF+fnctmgRZqmU6p4eQru7KTMaeePBBwl3dmZpTAzKjAzGBwTw486d/PzDD7xWW4vJZEJbXY2r\nnx+Ve/ZgkkjwS093/FJpbqalthZ1WJi93Wq1UlVXR9Yvv1De0YGLszOxl1+Oy4G3Clotnf2+RikU\ndLe10dnbi0dHh6168GHu00uvx7pwIZUFBUja25H4+REzYQJeZjMjVng7XT60Y83mWPNnJM6ENRhr\n/pwMm8KfsWdzrPkzAqek6BfFuU4dm2PFn9mzMygqyiY+Por09ExiYsYNu7t4OqzBSEW08PHBYrFQ\nWlFBVk4OtfX1AKhcJPi4aHFWepIYF0dmejrhISGUbNmCW2cnnX19qOLjhxjuBlTh4Sd8geQeHnTK\n5Xi4u2NydkYWFITcz4+YFSsoLy/n7ffeY8255zLZ2xtrVxflMhmTMjN585tveGnNGqxWK5etXctX\nJSWsvvxyjGlpSKVSZLLBeXWguq2NsP5CYDkFBWzbtYuWtjZ7alIDkFtfT2a67UyIl5cXrFgBA7IW\neWBLu8mBz9kh7lMK+Pr44BsTc3RrdDp8aMeizbHmz0icCWsw1vw5GTaFP2PP5ljzRxTnEpzu2OLQ\nC6moaGDZsvOG9Pv6+rN69c1ER58BKXgOQ6/BQM7OnezIy6NjUFhLTGQkLs7O3LxmjU2s9jN+5kwa\na2tRGI34RkUNmXN/Swsev0PmmVXLlvHOxo1cf/nl+Hp706zREOTnh0wm49/vv8/f7r2Xltpa9pSV\nYTSbiZowgVfefpuEuDiMRiNyuZzkhAT2t7WRX1RE6oQJw9opq64mfkC6zvKqKpvg78ddpWLqpEmk\nDKgOLJVK8XJ3pwNHOrRaW779Ud5jU20tHbt3Y3FyInz6dNxHGR4kEAgEAsFICNEvOKXR6brJz88m\nP38nPT069HqYOXMyfn4BQ6719DxzBX+bRsP23FzysrPpG1QoysfLi0y1mpTERP76xBMOgh9sh2jD\noqOHjaur2beP8JCQ41ZF1p7v/gBaLV5eXkilUiJCQ9nX0ICmvZ1l557L+//7HzePG8fn333HZStX\nolAoiBg/HsaPB42GJp2Ozu5url61imdefZXbr7+ebp2O9VdfzQOvvTas6Dcajbz8/vs89tBD9rbM\n9HRKKioIDQoiMzaWpClThn070NHVRee2bcPn2x/l/WuLikjoDxkqKyxk/PTpoxwpEAgEAsHhEaJf\ncMry88/fkpOzHYvF7NBeVlY0rOg/07BarVTX1pKVk8Oeqipbo9For44bExFBplpNfEyMXbQnRkez\nLTubaZMnj2r+f731FnevXWub9zjQodU6FKrqbGuDCy+05+G/a+1a7vvb33hw3Tqa29qwWCzszMtj\n+aJFDvPsb23l8bfe4pE778TdzY30xkZuvPtuLl68GKlUir+vL/tbWgj098dsNlNSXk59UxPf//IL\nt1x2GQrFwdykEaGhXL1qFTKLBTeLZVjBf4Bjzbdvlsuxms30mUzIBvhwyOvNZnp7elAqFMftwUsg\nEAgEpydC9AtOWZyc5HbBL5FIiI0dT0xMJsnJkSOMPL0xGo3kFJSStWsXzQPCUsBWUTdl4kQyJk2y\np9wcyOoLLuDhN95AqVAcMvwFbIL/mVde4fx58/Bwdx8xJeWR4CCcdTqHPnc3Nx65807+9s9/IpFI\nuP/vf7dX6wXbG423N26krqqapIQl/O+zWsbHWokOD6eru5vvfvmFyuJizp03j483bWJiQgLfbdnC\n1uxsjCYTrz35JFGDQmokEgntpaW4NzbSrNOhmD0bv8BAW9jOcc46Ez1jBuW7dyN1cSF64sTDXqvv\n6aF682Y8XVzY6+tL0qxZx9UXgUAgEJxeCNEvGPNY+4scDSYtbQr5+TtJTExh0qQMvLy80WjgTN3w\n7OrqJC9vJ8Xbf0Qi0Tv0uatUTElLIz0sDFVY2CHnkEgkPLBuHc+++irf/fILq5cvJzgw0N5vtVr5\nbedOPv3mGy664AKmTpp0wu7nULi7ufHXO++krriY259/ns++/Zbu/ocDhULBZStWkP3rHkw5Naic\nFezdtYf/1O3g0SefJCo8nOrdu/nriy/yy/bthIeEoHB1RT1xIgpXV5paWogdJPrNZjN9TU1QUkK4\n2UxjVxedwcG2sJ1RFN06EhQKBeMyMkZ1bWN1NeNlMmRKJdbWVnr0epSjeDsgEAgEgjMTIfoFY5b2\ndg25udtpbm7kkkv+3xDhr1K5cd11fz5suMWR0tBQx8aNb9PXZ0Aise3ims0mZs68kIyM5ONm53jS\n2FhPTk4We/YU2XLQ9+rtlXNDAgPJVKuZMH68bZ1GsSMvkUhYd+21aNrbeXvjRjQdHTjJZKDX0yuV\nMn3yZDbce++QXe7Ori7yioro1unw9PBAnZzsECYzWjoH5Mfv7O3lUEdZw4KCePYvf+Ffb76Joa+P\nAD8/LlmyhAA/P9xUe8hv20d+YyXurn08umIFrv1vD6LDwlg0dy6N+/eToVYDEBcVRaZaTWxUFLS3\nO9iRyWQYvL3BYqEPCI+IwDTMZ85isVCn0dA+4O1El8FAmMUy4pOoxWKhuqAAk05HSHLyqA7wevr7\n05CbS7BKhVYuJ3hQNWKBQCAQCAYiRL9gTGG1Wtm3r4YtW7Jobt6D1WoFYN++GiIioodcf7wEf29v\nLy+++DgREUH88Y/X4+Fx8DCrwWDggw8+5ttv3+C66/5MQEDQcbF5LFgsFsrKSsjJyaKhYZ9Dn0Qi\nYcK4eDLU6iM+ZGuxWOgYIHpXL18O2FJPSjs6hk0TVrxnDx988AEunp5MS08n0N8fbWcnT778MlKp\nlEvnziV6lKkIvTw9YUB8vkdHx5CDxQPx9/XFbLHwyPr1NDU38/FXX6Hp6KCvsxNfl07uODeDuAh/\nuoYptBUWEsLklBQy1GqHEKHhGJ+RQVdrK95yOS6enmi6u4e9zglwGrDeo/2CrSkqIqSuDoVcTvFv\nv5E06IzCcPgGBCDJyGCf0UhMdPRxffgVCAQCwenHKSn6RXGuU8Pm0fjz9dcfUFVVjE6HvZCWVCqj\nuroFN7foE+JPX5+BZ565myVL7iQxMRiTafBnzIXJk1dx9tnLePrpe7nqqjvx8zso/I9lDQ4U0ios\n2c3Xm79CJpUh7Rdv7W060lKTOG/BYpzltl1cfa+ewpLd/LYtGzOOwtPF2ZWUpFSmBgUQE9MfdjJo\n13okhzqqqujMzx+agWb+fHz6K8YO5L1Nm2hqbeXcxESMHh54SiQkBQcjCwvj7KQkdD09vPDqq6Tt\n3cs5M2aMuEBScMx0YzBAx+BEmAfHygA3iYT26mqCPD25YfFiAGry8/n1m2/YVllIoCrFXijLYjbT\nUVXFtt9+4+Grr8bF2Rkvq3VIca7O/sq9Xv077hKtFnp70Wm16IxGx8JbB3xvayPExQWfAUXKNFYr\ndHba7uNQNDdjam7GRa8HgwGZwTDEn0PhYzbjExRkqx+gdwzpOm2+SE6GzbHmz0icCWsw1vw5GTaF\nP2PP5ljzZwROSdEvinOdOjaP1J+kpGgaG4sB8PNzIzV1Cqmpk1EqVSfMn3/84yluvfV2XFyCRxir\n4MEHH+Xvf7+fBx548rj4Y+zZz7P/fIYpqalsuGstcrn8YKdGw+6mJl585WHmzZyJ2Wwmr6gIo8mE\nEluRKABfb28y1WpSk5Jwdna2CcajdcjfHw9fX8cMNN3dtqJTVqvD2H++/jo/b9tGSlISrVIpKqWS\nco2G97dswcfLi8tWrsQ/LIz1t9zCi599hltlJTOmTDkyf0bq9/Fh9erV/PuTT7jjhhvYV19PVk4O\nxfn59PTfQ3NmJgF+fnh5etKh1bLru+8INxpxKSo6mFJzQGz+vupqpCUlyICq2FhiUlJsxbcuvND2\nAOLlNbTwVv/aUVcHg7P3DLN2gwmPjKT0t9+Q9fXhrlYPvXas/+GejjbHmj8jcSaswVjz52TYFP6M\nPZtjzR9RnEsw1tDrewDlkPakpFT27CkmPDyNKVMm4OR0Yj+iXV2dSKVSgoNDR5WARqFQoFZnUlCQ\nw8SJ6mOyrdfr+fvzG3h0/a34DvMHbLVaUSmVpCYl8fQrrxAREkJYSIi9PzYykky1mrjo6N89XeOb\nH33Ep99+yyevvWaL2x/woLHivPNobm3lmVdeYfmiRUyJiODGK67gnieeYPrkycfd19DgYCJCQrj1\ngQccQoEUCgVSqRSdXm8X9XWNjXxeWMizV1xxyMw7vY2NxPe/ZipvaoKUFKRSqW0OqxUOc3i3c1AY\n0eHOJAxEoVCQNH/+KK4UCAQCgeDoEKJf8LthsVioqCglJyeLjo52Vq68FXCMQ3Z2dubii69Ao7Gn\nkz+hbNz4DsuXXzqk3WAw0NjYir+/FyqV41uGc85ZynPPPXLMov/f/36Wm9esHSL4jUYju0tKyPrl\nF1r6ReS09HS+27KF8JAQ1BMnkhEdTUB8/DHZPxQjCdeNmzaRW1DAhnvuOeRB3QA/Px696y4efe45\nVDNmkDRlCtPT09mek0NmevqofWnXavno66/Z39KCVColOiKCZeecg+uA8COAS5YupbSigp+2bWNS\ncjKBKhWTp05lSloaHu7u9Pb28uGXX5JfXMw9CxceNtWm3M+P5qYmnCQSJBERo/bVy93d4TwCcPCN\nwKFClAYxpDgZOBQoEwgEAoHgaBGiX3DC6e3tpaAgh7y8HXR2HhQ/lZXF+PsfPhf5iUajaSU01FHY\nmUwmvv++AoslCrO5gXnzgoGDIRtyuRwnJznHgl7fg8lkInBAEbHOri525uWRvXs3+t5eW4x2v6j2\ncHPj2tWrkUilLF64cMjBFr1ez4dffkltfT3Wnh5QKJgzfTozpkw5op31kYRrb28veUVFuKlUpCUP\nzWZUtXs35rY2XEJCiBg/nntuvpl7H3qIx6dM4bx583jkuedGJfqra2v5v//9D3eplD/84Q+EBgdj\ntVoprajgsRdewEUuZ+2yZXgOeGD68/XX4+Huzv6WFjq6usjKyWFHXh6Gvj6kUikXXXAB5519Np0f\nfmg/iDvcTnzUuHG0xcVhNpuJG5CudCTsbwOOgcHFyWBogbITzeDD3Ac4EXUJBAKBQPD7IUS/4ITz\nxRcfsHdvpUObl5fPmM020tOjo6/PH5VKhdEYzv79zfj4HH2V1eH4/PMPWLLkEgDqGxvJysmhaM8e\nDAYDLW06VEo5nnIpoUFBZKrVJI0bh0wm474NGxzmMRgMPPfaaxgMBi5avJg/XnghaDSYPT355qef\nuG/DBianpg6pWHsoRhKu73/+OauWLePDL74Y0teyfz+eVVX4KpXUFxfTGRKCh7s7CdHRlFVUMD4u\nDtkoRGNOQQGffP019996K/KuLvDxwWq1UlFdTU5BAUF+fqxatoyHHn+cO2+7zV5HwE2lYt211wJg\naW2lXSrFbDbbQ8TsoT/z59vi7BnwQDMIXz+/Ef08UQyp6juoQNmJpqOri4+3KVG6utvbenq7WLFI\n+7s9eAgEAoHg+CNEv+CEk5KSbhf9ERExqNWZxMTE095+8qtoWQdkXzmASuWGSrUPnU6KTNZCSEgU\ngzM+DjfuSKivryUlJZ23Pn6Xdm2Dfc7y6i6cZAEolUEsmhnG1LOmO+zUKwbsAPfo9dzz+OPcccMN\nhAYHO8wvk8k4b948zps3jy+//55/vv46N1555TH5DFBRU8PlF100pL2quJjmlhZ8W1vxjXSsiHzx\nOefw7EcfcfdNN404f11DAx9/9RV/vfNOJBIJfUYj+Xl5fP7ZZ7Tv24fZyQmfqCjatVoev/VW7nrx\nRTbcey8uLi4O83R0dbFxgHAdKFp9PD0PG5cvAKWrO+5ug9foMBmIBAKBQDDmEaJfcFwwGo20tOwn\nJGRotde4uATS06cxYUIa/v6jD5f4PQgLi6SiopS4uAR7m0wmY968RDo7tahUsTg7OzuIfv3g1Ij9\nlJeXsGvXNnS6btzdPZk1az4uLqEO1+j1egoKdpGdvRWLxYxM32UvpOUslxMfk8HE8eegcHVDJtt1\n2NCcR559lntuvpmAEXalz58/n69++IGPv/qKFZmZI6zI4XHuzy5kNJnsbU319XjX1hLj58eWvXsx\nmEy4JyTg4W4T3ApXVywWC2azGbPFctj5X3//fe65+Wb7fb/5+edUt7XRvW8fvnI5FqsVc28vhr4+\nXN3dufayy/jgiy+4bOXKIXMNFa6nhmg92sPAAoFAIBAcDiH6BceETtdFcfFOdu/Oxmw2c+21tw3Z\ndZVKpcyZc85J8vDwXHDBRbz88pOsW3e/Q7tMJsPbe/iUWJ9//j6LF19s//nHHz+jvHw7cXEJzJlz\nLkqlCq22nW+++YSamgYWL15MVFQsOTnbKS7Ox2Qy0tdnsFXPBfx8fMhUq0lJTGRHXh37Gjowmfcx\nIXaomD8gmosrK0kaN25EwX+A8+bN494nnmB5RgbH4/1KUnw8+UVFpE6YgNloRN4fthMdGYnPnDmo\nlEMzM339448smDXrkHPq9XqsVivKAYeDU8ePZ29WFkaJhHAfHxJCQ4latIjQqCjQaEgaN463P/74\nONzR2GBwcTIYuUDZsTDcweH2zk66exwPSvf0dgEnvuKvxWKhq7sblVJ5wjN3CQQCwZnGKfmtKopz\nnXybzc0N7N6dRV5eIUrlwd3brVtzSU3N/N39Ofo+BTKZB9nZJbi5JR5y7IEiWh3aDioLc7l4/gVY\n21p56f+eJzwwibuuve3gxSYTHip3rlh8CXm5Fbzzxot0aDtIiD84f0RgCDpNC9fMX8TktP6Um93d\nzIzzwhDRh9zJC2lrKwwI5+kzGrHodKDR8OHGjdx5001D/hgsFgvGhgYcH7tszJ4wgV9++IHZh0sN\nOcICGru6QKNheWYmj77yCqnBwYS4u7NHJkPa3Y08JARVby8DX40YGxqw6HT8umULj91yy5DCU2az\nmbaODrYXFHDe5MkO/SleXuR4+uGdkIjJ0IlHQhihHh62a/p9jfHzY19xMeFBAyolt7SAzg3oP3Ct\n00FHty3l5hF+MC0WCx1dXQfn9fcHbIeeHQ62HocP5pDiZDBigbJjsdmh1dL5/fcOB4cl+/czb8EC\nvD0c3zh4mWUO6368/bFYLJT8+iv+3d3Uy+XEzJ6N64ENhLHxZXFybY41f8TvRKzBWPPnZNgca/6M\nwCkp+kVxrpNv85dffmHfvhKUSluCGYlEQlxcIgkJ4Q7XjrU1GK5v7dqb2LDhXhYsuIKEhIShFwAu\ndAMa/vV/z/LoHXfg4W7gpTfe4IK5k8mMjgafg6EjfX197C4pYXtuLi11dcRHBlBQqqGpqYKk+HhS\nk5KYOukynvv3v5kyKWaIU3bBLpU69H3y2WcsW7bM1qZS4Toojl+v17Ppx3r62t0I0bUze1qsQ/+C\n88/nsQ0bmH3xxRyWwyxgcGQktXo9EaGhpE2ezEfbtrHy/J45dZoAACAASURBVPMZP2/eIcd99t13\n9EqlnHf++Uh8fe3tPXo92bW17PzpJwA8PTxIj493mMcZCIxwRyodD0CzUymDP2AhMTE0m0yED2z3\n96enwgIYbbZkFtvh3QOx/Efwwexob6dz2zabMNbpoK5u2KJeI847Fv5wB/dJJEOLsQFERBz+0O4J\n8KfLyQl/q5WAgADc+/po6ekhfOBnfCx8WZxsm2PNH/E7EWsw1vw5GTbHmj+iOJfgeJOenklFRQnO\nzq5MnqwmLW0qnp5eJ9uto0IqlXLnnY/w/PP/4LffPmLp0j8QFXVQMLe1tfDZW6/SZ9Ty6Pr1uKlU\naDs70XZ12dJP9u9Mazs72ZmXx66CAlvKzQHMmDyZvKIibr3mGnvoS4ZazaebN7P0wgtH9LFVo2FX\nQQEXL1li93kwpRXNSKUJqBR66ps60Ov1Dnn0j0e6xUuXL+fF11/n7ptuYvmiRbz10Uf86803uWrh\nQoZLYtrT08OL773H7WvXMisjA4Dm1laycnLYXVyMqbvbnpbU0NdHn9E4ZA4XFzO9BjMWswmV79Dg\nJENfHy7OjqEnXu7urFgk42AcvzNenp62lKKlpZjDwhiXmTnqNXHIqDNYIAuOC0qFgnq5HA+jkXqT\nicD+NyoCgUAgOD4I0S84JBpNGw0NtYSETBrSFxoawaJFK/D1TSAw8MTH+p5oZDIZl1++DldXHZ9+\n+h4bN76NRGIThEqlij+ct5yE+IPx8+9s3Mjq5csBqGtqImvrVorLy7EMOqgaFhhI5qxZJMbH88Ov\nv5Kdn89Z06YBsGThQl595RU++fprlp177iF9a9y/nydefJFH16+3tw13wDfAz42yymYUFhXOik6c\nnYfuBBxrNVwPd3cC/Pz4aetW5kyfzmUrV7K7uJhHX30VpY8PM6dMwU2loqOzky1ZWWzPzeX6iy/m\nvHnz7HN8+cMP7K2rc5h3XEwMPl5e7Nq9m6jwcIe+udPDyC2swFkuIXWCYx9AWWUl82bOdGgbLvWo\nrqcH58pKwpyc0Gk0NNTUEBYTc0zrITh+yOVyYubPp6W+nkB/f9w9xPFlgUAgOJ4I0S9wwGq1sndv\nFTk5WVRXlyOVSlm5MhYfH8d/gCUSCUlJKcOerziVUSpVrFq1Zki7i6bB4efG5ma0XV1888471FVV\ngUKB1Wqlbd8+ZH19xE2YwPIVKwhzdbW/hps/axZ/ffZZu+gHuObCC/ksO5t7Hn+czPR0zp83z16/\noKy6mvffeAMnJyc23HOPQxXawW8SAEKDfZmV0UZjRRmJ6fFD6iBU19YSOCC85mhZs2oVz//nP7Rq\nNKw8/3xSkpJICQqiQyYjp6CA1vZ2FK6uODk5ceMVV7AoLc1hfKZazd66OpzlctJiY8mYPdtelfje\nJ55g5fnnO1zv4uJCZnrUsL4YjUZ69HrcR7H7LpNK6ZNIwGql12xG7jLcyYfhsWfU6c+ZPzijjv1A\n7KCHqlOhoNVYyhbk6upKeGzsyBcKBAKB4IgRol9gp6gon507f6WtrcXeZrFYKCzcSUTEvMOMPHPo\n0evZtXs3O/Pyhog5Y28v54SGctb48XRbLLi5u8OAcBWpVIrTMAXJlixcyOIFC9iRm8vjzz8P2B6q\nwj09uf366x1CdA6gTkoiOz+fyampDu2hwb6Eukiw5wEdwLuffMLNK1Yc1X0P5qarruLnbdu4/29/\nIyQwkNmJiaiCg/H08GBHXh4dnZ2clZHBorPPHnLYeHxsLOedfTYTExJQ6PUOsYnjY2PtWYFGw3uf\nfsryw7wlGYirqyuqyZMpLyjAOTqayNDQkQcxKKNORwd4eQ0p6mU/EDvgoeqQcf9jiN87W5BAIBAI\nTh5C9AvsNDbWOQh+lcqdSZOmEhaWfhK9Ghu0atrYmr2L3SUlGE0mDH199j5/b28yZs4kxMcHp127\n8FAoaOvpwdvJyUH0Hw6JREKGWk2GWn2wUaOxx7sPZsmcOdz18sukp6SMKmSnubUVi8WC2zCpNI+W\ns6ZN46xp06hraCD7t9/orqvDaDIRFBCAoa+P3KIi5s6YgeugcVKplKmT+kPGBtU8uGzlSu567DG8\nvbyIGEaUD0wxufO336itr+f8efOwWCyj2lEPDAsjUKk8ogOTDqFCVushC3sNqaR7CjBsBWar1XaI\nXCAQCASnFUL0C+yo1Rnk5+8kKCgUtTqTceOSkMlkp10Iz2ixWq1UV1eQk5NFXWmew+a52WwmPjqa\nTLWaGHd3e1aafQYD5fv345GUZNuhHyBqe/T64xbq4eTkxKply9jw4ous/9OfDiv82zs6ePQf/+Dx\nu+8eIrKPB2EhIbSHh7OtooKG/fvt7X1GI7mFhUw7TNx8n9GIrqMDTw8PpFIpUqmUR+68k4eeegr1\nxIksnTLF4YBwh1ZL4yefsGn3box6PdcvWEDnpk1jfkddIBAIBIKTjRD9ZxAWi4WyshIaGvYxd+7Q\nkAgfHz+uvPJP+Pj4HfOBz1OZvr4+iovzyc3djkbTCsCBoBy5kxNpEyYQExHBuJgYYvuLRB0gPC4O\n4uKGnff9zz7jwkHx6mXV1ZTv2oXZYiE4IIDJqamjfjBIT0nBarVyx1//ymUrVpCWnOzQbzQa+fSb\nb9iem8tf77jDVvTqBIh+gJKqKgfBr1QomJySQnJCAgx4KzKQ7u5u6n74AT+FghJPT5LmzkUikeDs\n7Mxjd9/Nrt27eeSVV3Byc8Pf1xezxcLefftwrqvjqlmziFEqwc3tjP6sCgQCgUAwWk5J0S+Kcx1Z\nX2+vnpKSHLZt2wHYQiNCQ9Pw8wsaMlYi8ae9/cT7eqLmPVBEa/iBZg7K96F0VbVTuDWf3cX5GPoc\nDzd6IGFuWirqxEQUrq6YTCbuf+EFpkREjMpZk8lEaWEhVy5YgKW1lQ++/Zb8sjIS/fxIVqtxksmo\n3buXBz79lCA/P65YuhT3/kOjh5t3cng4qdddxyebN/PBhx+idHXF2t2NRaHAbLGwdO5cLvzTn2xh\nRiMVVhow75H2ZYaGUlxVRaCvL5kpKSTHxSGXy22C/xDjmktLie/pQebqiqG+np76eodKvulhYaRf\ndBEmHx86urqQSaWYLRakW7fio1QeLFil09n+32o95vs46rFaLZ2D+jp7e/E44NdY+0M5lfw5GTbH\nmj8jcSaswVjz52TYFP6MPZtjzZ8ROCVFvyjONfq+3377kezsrZhMNiF8IES8qamAceOCDjv2RPed\niHld6Mb3EH0haIYMtFqt1DU0kJWTQ8nu3VhcXXGWgXP/OoWHhJCpVpPo64vU72DKTidg4Tnn8MpX\nX3Hteecd1lmLlxd/efJJrlmzhl6lkgeefJKLFy/mkksvtQnx/rETgfOXLaOhqYkHXnqJP19yCWGj\nWAQ5cNGqVVx0oF0z9D6HG3ck/VarlT3V1TSXl9vz7Q8kPDmZNXFxhIWEDL/z3j9nXVUVvV1dRCQm\n4puYyN7KSgLlcjr8/QkOCRk2ltzJxwe/gADbrbW3g0p1MFf+gf8OLLw1mvs8zh9MLy8vWLHC5kc/\n9sO+B+5pLP2hnGr+nAybY82fkTgT1mCs+XMybAp/xp7NseaPKM515iKRSOyCHyAyMha1OpPo6OFD\nUM4UzGYzRWVlbM/Npb6pydbYv1MslUqZMG4cmWo1oQcqgg7zemnujBkY+vp4+KWXuPGGG/Ab5o+w\nuq6OF194gSsuuoiYyEjueuwxblmz5uC8wxASFMSGe+9l/f338+A995zUTCoGg4G8oiK25+aiaWxE\nqlKRkpiIp8fQFK7hI2TD2VtWhteePQTL5ZS2tDBh/nykZ51Fh8VCfEjIqMOaRkqfeTKQSqX4eP5/\n9u48vK37PPD9FxsJECvBVRTFTVzFndRC2ZZix3JqNU6zO50kbTzTie1p06ZNmm43uZnp1DeTp076\nTNNpOtPbzE26pO1k0qTT2Glqu7FlWdRCUiQliqu47wsWEvty7h8AKYKLuBMA8fs8j59HPAcHvxcH\nIP3inN/vfY2bLvIVBEEQhGgTSf8RIUkSsP4Ka23taVpbmyktraSw8BwlJZmHH1wMcbpc3Gpv5+bt\n2yyumT6jSU7m9NmznKmrw6DXb+v5nn7iCeqPH+c7/+t/YbHZKCksRKfVYrHZGBgaojAtjS9/7nPo\ndTreePttLl248NCEf1lSUhJffOEF/t/vfpfffPHFXb3WvXrj7be53tYWUakoGAxy++7diF4D2+VZ\nWMAYro2vCq8t0Ot06HdwdXM75TMFQRAEQVhPJP1xzm63cfv2TSYnR7l06TnWJv5arY4XXvg8KpUq\nYavwQKhkZfObb9IxOoo/EIjYl5mWRlNjI9WZmaiysnb83Fnp6XzuhRcIBAKMTU7icDox6vXkZGcj\ns1hWpqC8/vbb/MGqrrpbSTOZsC8u4vP5QvPjD5nb44lI+POPHaPp4kXKdtk8Kau8nHtXrqAOBlFt\nsth5K9stnykIgiAIQiSR9MepiYkxWlub6evrIhgMAjA2dp+0tPUJWTQSxlggSRJ99+/T3NrK/ZGR\nUOWaVXXvS4uKOFdfT1F+fmge+h6/FSkUCvJzczfcZ7FaSTeb1813d3s8vPXmAH6/nNO1JjLTI5PY\ny088wWtXroSaXB2yc/X1tHZ2UllWRlNDA8dUqj3NOTampqJ773sJBAIkJSXtY6SCy+VCpVKhVIo/\n6YIgCMLGxP8h4tArr3yfe/c6IrbJ5XLm56cB0cLe6/WuzEOfX1OKKEmloq6yknP19aTtZdHcDk3O\nzGz4heBG5wyu4GnkcjnXbnXz/qcjk/6TBQX84Mc/PpCYlhwObrW3Mzs/z0ff9751+9PMZn7zxRdR\nq8PttfbhVpFCoUCxQVdiIVThaaizEykQIL+mZttfjPpaW1EPD+OQy8m+cAHTIX6uBUEQhPghkv44\nlJNzYiXpV6s1VFc3Uld3Br8/sec122xW7r7zJv1Dnbg9noh9JoOBs/X1NDzyyIMk9hDJZLLwu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CgcFkWonVp9fj9njQabXrFxMf8Hsyee8ep8LntGd8nGPnzh3Z35MjO2asxbOVRDgHsRZPNMYU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\niFSOSz8RERERkcr9C9k3+UTMlS1UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4b8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "points_plot(ax, Xtrain, Xtest, ytrain, ytest, clfsvm);\n",
    "plot_svc_decision_function(clfsvm, ax)\n",
    "ax.scatter(clfsvm.support_vectors_[:, 0], clfsvm.support_vectors_[:, 1],s=200, facecolors='none')\n",
    "plt.ylim([125,225])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##The confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have written two classifiers. A classifier will get some samples right, and some wrong. Generally we see which ones it gets right and which ones it gets wrong on the test set. There,\n",
    "\n",
    "- the samples that are +ive and the classifier predicts as +ive are called True Positives (TP)\n",
    "- the samples that are -ive and the classifier predicts (wrongly) as +ive are called False Positives (FP)\n",
    "- the samples that are -ive and the classifier predicts as -ive are called True Negatives (TN)\n",
    "- the samples that are +ive and the classifier predicts as -ive are called False Negatives (FN)\n",
    "\n",
    "A classifier produces a confusion matrix from these which lookslike this:\n",
    "\n",
    "![hwimages](./images/confusionmatrix.png)\n",
    "\n",
    "\n",
    "IMPORTANT NOTE: In sklearn, to obtain the confusion matrix in the form above, always have the observed `y` first, i.e.: use as `confusion_matrix(y_true, y_pred)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 84,   8],\n",
       "       [  5, 103]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, clflog.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given these definitions, we typically calculate a few metrics for our classifier. First, the **True Positive Rate**:\n",
    "\n",
    "$$TPR = Recall = \\frac{TP}{OP} = \\frac{TP}{TP+FN},$$\n",
    "\n",
    "also called the Hit Rate: the fraction of observed positives (1s) the classifier gets right, or how many true positives were recalled. Maximizing the recall towards 1 means keeping down the false negative rate. In a classifier try to find cancer patients, this is the number we want to maximize.\n",
    "\n",
    "The **False Positive Rate** is defined as\n",
    "\n",
    "$$FPR = \\frac{FP}{ON} = \\frac{FP}{FP+TN},$$\n",
    "\n",
    "also called the False Alarm Rate, the fraction of observed negatives (0s) the classifier gets wrong. In general, you want this number to be low. Instead, you might want to maximize the\n",
    "**Precision**,which tells you how many of the predicted positive(1) hits were truly positive\n",
    "\n",
    "$$Precision = \\frac{TP}{PP} = \\frac{TP}{TP+FP}.$$\n",
    "\n",
    "Finally the **F1** score gives us the Harmonic Score of Precision and Recall. Many analysts will try and find a classifier that maximizes this score, since it tries to minimize both false positives and false negatives simultaneously, and is thus a bit more precise in what it is trying to do than the accuracy.\n",
    "\n",
    "$$F1 =  \\frac{2*Recall*Precision}{Recall + Precision}$$\n",
    "\n",
    "However, in a case like that of a cancer classifier, we will wish to minimize false nagatives at the expense of false positives: it is ok to send perfectly healthy patients for cancer folloup if that is the price we must pay for not missing any sick ones.\n",
    "\n",
    "`scikit-learn` helpfully gives us a classification report with all these numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      0.91      0.93        92\n",
      "          1       0.93      0.95      0.94       108\n",
      "\n",
      "avg / total       0.94      0.94      0.93       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print classification_report(ytest, clflog.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####The cancer doctor\n",
    "\n",
    "Do you really want to be setting a threshold of 0.5 probability to predict if a patient has cancer or not? The false negative problem: ie the chance you predict someone dosent have cancer who has cancer is much higher for such a threshold. You could kill someone by telling them not to get a biopsy. Why not play it safe and assume a much lower threshold: for eg, if the probability of 1(cancer) is greater than 0.05, we'll call it a 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def t_repredict(est,t, xtest):\n",
    "    probs=est.predict_proba(xtest)\n",
    "    p0 = probs[:,0]\n",
    "    p1 = probs[:,1]\n",
    "    ypred = (p1 > t)*1\n",
    "    return ypred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how the false negatives get suppressed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 69,  23],\n",
       "       [  3, 105]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, t_repredict(clflog, 0.1, Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##ROC Curve\n",
    "\n",
    "The images in this section are from Provost, Foster; Fawcett, Tom (2013-07-27). Data Science for Business: What you need to know about data mining and data-analytic thinking  O'Reilly Media. Great book!\n",
    "\n",
    "We can check on the thresholds we talked about in the previous section and compare our classifiers to each other and the baseline models using the ROC curves you learned about in class. \n",
    "\n",
    "Remember that ROC curves are actually a set of classifiers, in which we move the threshold for classifying a sample as positive from 1 to 0. Each point on a ROC curve is a separate classifier obtained by considering a different threshold. (In the standard scenario, where we used the  classifier accuracy, this threshold is implicitly set at 0.5, and we have only one classifier).\n",
    "\n",
    "![m:roc curve](images/roc-curve.png)\n",
    "\n",
    "The way ROC curves are calulated is this. We start with a large threshold, something like 0.99 or so. This means that only samples with a probability of being positive higher than that threshold are classified as positive. That is the really really really positive ones! The idea then is to decrease this threshold, such that more and more samples get classified as positive.\n",
    "\n",
    "![howto roc](images/howtoroc.png)\n",
    "\n",
    "The practical way to do this is to order the samples by probability of being positive, or in the case of the SVM, by the `decision_function` or distance from the separating hyperplane. Then consider the sample with the highest score or highest probability of being positive. At first, only this sample is positive. Then, we take the sample with the next highest score, and call it positive. As we go down the list, we go down a threshold in score or probability. \n",
    "\n",
    "Now, for each such situation: only 1 positive, now 2 positive,....you can imagine a different classifier with a different confusion matrix. It will have its own false positives, tre positives, etc. Its actually the same original classifier, but with a different threshold each time.\n",
    "\n",
    "As we keep going down the list, decreasing the threshold, more and more samples become positive, and at first, the true positives rise faster than the false positives. Once past a certain point, false positives increase faster than true positives. Now, if you want a balanced classifier, you look at this turn-around point...the northwest corner, so to speak. But if you want a classifier which penalizes false positives and false negatives differently, the point you want is different.\n",
    "\n",
    "Here is the confusion matrix again:\n",
    "\n",
    "![hwimages](./images/confusionmatrix.png)\n",
    "\n",
    "\n",
    "To make a ROC curve you plot the True Positive Rate, \n",
    "\n",
    "$$TPR=\\frac{TP}{OP}$$\n",
    "\n",
    "against the False Positive Rate,\n",
    "\n",
    "$$FPR=\\frac{FP}{ON}$$\n",
    "\n",
    "as you go through this process of going down the list of samples. ROC curves are useful because they calculate one classifier per threshold and show you where you are in TPR/FPR space without making any assumptions about the utility matrix or which threshold is appropriate.\n",
    "\n",
    "Notice that the ROC curve has a very interesting property: if you look at the confusion matrix above, TPR is only calculated from the observed \"1\" row while FPR is calculated from the observed '0' row. This means that the ROC curve is independent of the class balance/imbalance on the test set, and thus works for all ratios of positive to negative samples. The balance picks a point on the curve, as you can read below.\n",
    "\n",
    "A rote reading of the ROC curve (go to the \"northwest\" corner) is a bad idea: you must fold in the curve with any assumptions you are making about costs. More on this in the next lab. Still, on the whole, a curve with a greater AUC (area under curve), or further away from the line of randomness, will give us a rough idea of what might be a better classifier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):\n",
    "    initial=False\n",
    "    if not ax:\n",
    "        ax=plt.gca()\n",
    "        initial=True\n",
    "    if proba:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n",
    "    else:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    if skip:\n",
    "        l=fpr.shape[0]\n",
    "        ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    label_kwargs = {}\n",
    "    label_kwargs['bbox'] = dict(\n",
    "        boxstyle='round,pad=0.3', alpha=0.2,\n",
    "    )\n",
    "    for k in xrange(0, fpr.shape[0],labe):\n",
    "        #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n",
    "        threshold = str(np.round(thresholds[k], 2))\n",
    "        ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n",
    "    if initial:\n",
    "        ax.plot([0, 1], [0, 1], 'k--')\n",
    "        ax.set_xlim([0.0, 1.0])\n",
    "        ax.set_ylim([0.0, 1.05])\n",
    "        ax.set_xlabel('False Positive Rate')\n",
    "        ax.set_ylabel('True Positive Rate')\n",
    "        ax.set_title('ROC')\n",
    "    ax.legend(loc=\"lower right\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot ROC curves for both our classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0VHvw0ot/4B9/e4XsnIbRiatm/izxoXJ18QqeWvwIc+YuTOxvYLRp5eRU1ib4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b457290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "ax=make_roc(\"logistic\", clflog, ytest, Xtest, labe=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1094b2cd0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WBHCnu/H5Q1TWhLBMiw0lPmyBjbz81BwqykvZXF5DyFYdW2dDiY9l/3kTT4cC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\nJEmSJKXqF/neD0/miNDFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b478610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_roc(\"svm\", clfsvm, ytest, Xtest, labe=10, proba=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "\\cal D": {},
     "\\cal E": {},
     "\\cal H": {},
     "\\cal L": {},
     "\\ell": {},
     "\\mathbf #1": {},
     "\\mathbf x": {}
    }
   },
   "source": [
    "##Loss Functions\n",
    "\n",
    "\n",
    "$$\n",
    "\\renewcommand{\\like}{{\\cal L}}\n",
    "\\renewcommand{\\loglike}{{\\ell}}\n",
    "\\renewcommand{\\err}{{\\cal E}}\n",
    "\\renewcommand{\\dat}{{\\cal D}}\n",
    "\\renewcommand{\\hyp}{{\\cal H}}\n",
    "\\renewcommand{\\Ex}[2]{E_{#1}[#2]}\n",
    "\\renewcommand{\\x}{{\\mathbf x}}\n",
    "\\renewcommand{\\v}[1]{{\\mathbf #1}}\n",
    "$$\n",
    "\n",
    "###Estimation Loss\n",
    "In the previous lab, we'd introduced the log loss that is used in Logistic regression and noted that it is a loss for probability estimation...and not a loss for making decisions. We'll go into these dual losses (we alluded to these in the previous lab as well) in the other notebook in this lab.\n",
    "\n",
    "$$R_{\\cal{D}}(h(x)) = -\\loglike = -log \\like = - log(P(y|\\v{x},\\v{w})).$$\n",
    "\n",
    "\n",
    "Thus\n",
    "\n",
    "\\begin{eqnarray*}\n",
    "R_{\\cal{D}}(h(x)) &=& -log\\left(\\prod_{y_i \\in \\cal{D}} h(\\v{w}\\cdot\\v{x_i})^{y_i} \\left(1 - h(\\v{w}\\cdot\\v{x_i}) \\right)^{(1-y_i)}\\right)\\\\\n",
    "                  &=& -\\sum_{y_i \\in \\cal{D}} log\\left(h(\\v{w}\\cdot\\v{x_i})^{y_i} \\left(1 - h(\\v{w}\\cdot\\v{x_i}) \\right)^{(1-y_i)}\\right)\\\\                  \n",
    "                  &=& -\\sum_{y_i \\in \\cal{D}} log\\,h(\\v{w}\\cdot\\v{x_i})^{y_i} + log\\,\\left(1 - h(\\v{w}\\cdot\\v{x_i}) \\right)^{(1-y_i)}\\\\\n",
    "                  &=& - \\sum_{y_i \\in \\cal{D}} \\left ( y_i log(h(\\v{w}\\cdot\\v{x})) + ( 1 - y_i) log(1 - h(\\v{w}\\cdot\\v{x})) \\right )\n",
    "\\end{eqnarray*}\n",
    "\n",
    "where\n",
    "\n",
    "$$h(z) = \\frac{1}{1 + e^{-z}}.$$\n",
    "\n",
    "###Decision Loss\n",
    "\n",
    "When doing cross-validation, `sklearn` will instead use the 1-0 loss or the attendant `accuracy_score` as a way of finding the best model.\n",
    "\n",
    "From the last lab:\n",
    "\n",
    "The 1-0 loss:\n",
    "\n",
    "$$l = \\mathbf{1}_{h \\ne y}.$$\n",
    "\n",
    "where $h$ is the classification **decision** we make (for regression we used $l = (h-y)^2$). The symbol $\\mathbf{1}$ means that if $h$ is not equal to the \"true\" value of the point $y$, penalize by 1. Then the risk is:\n",
    "\n",
    "$$ R_{\\cal{D}}(h(x)) = \\frac{1}{N} \\sum_{y_i \\in \\cal{D}} l = \\frac{1}{N} \\sum_{y_i \\in \\cal{D}} \\mathbf{1}_{h \\ne y_i} $$\n",
    "\n",
    "Thus if 5 out of 50 samples are misclassified, then the risk is 0.1. This of course means that 90% of the samples are correctly classified. This number is called the **accuracy score** or **utility**:\n",
    "\n",
    "$$ U_{\\cal{D}}(h(x))  = \\frac{1}{N} \\sum_{y_i \\in \\cal{D}} \\mathbf{1}_{h = y_i} $$\n",
    "\n",
    "###SVM\n",
    "\n",
    "The SVM, being a classifier, uses by default the 1-0 loss in the validation part. But the estimation loss for the SVM is called the hinge loss. A bunch of losses are shown below: the red log loss, the black 1-0 loss, the green L2 loss (used in regression), and the blue hinge loss. so called, because of its shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![losses](./images/losses.png)\n",
    "\n",
    "(The image is from Bishop: Pattern recognition and Machine learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hinge loss comes from inspecting the structure of the minimization problem the SVM solves:\n",
    "\n",
    "$$\\cal{l}(y) = \\max(0, 1 - y \\cdot h) $$\n",
    "\n",
    "where Note that $h$ should be the \"raw\" output of the classifier's decision function, not the predicted class label. E.g., in linear SVMs, \n",
    "\n",
    "$$ h = \\mathbf{w} \\cdot \\mathbf{x}$$\n",
    "\n",
    "(taken from https://en.wikipedia.org/wiki/Hinge_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the solution of the optimization problem(taken from http://math.stackexchange.com/questions/327807/why-hinge-loss-is-equivalent-to-0-1-loss-in-svm):\n",
    "\n",
    "$$\\min_{\\mathbf{w}} \\sum_{i=1}^N [1-y_i h(x_i)]_+ +\\frac{\\lambda}{2}\\|\\mathbf{w}\\|^2 $$\n",
    "\n",
    "Setting  $\\lambda=\\frac{1}{C}$, this is the same minimization problem as\n",
    "\n",
    "$$\\min_{\\mathbf{w}} \\frac{1}{2}\\|\\mathbf{w}\\|^2 +C \\sum_{i=1}^N \\xi_i$$\n",
    "\n",
    "where\n",
    "\n",
    "$$ \\xi_i \\geq 0,y_i h(x_i)\\geq 1-\\xi_i ~ \\forall i, $$\n",
    "\n",
    "the $\\xi_i$ are the slack vectors."
   ]
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